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The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO 2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications. [Display omitted] • Effective connectionist tools are used to assess H 2 storage with MOF. • LightGBM model exhibits a better prediction accuracy, compared to RF. • H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy. • Increased surface area and pressure lead to higher H 2 uptake. • Data pre-processing steps considerably improve the model predictive performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two integrated configurations for H 2 production from solid oxide fuel cell waste heat are compared. • PEM electrolyzer and Cu-Cl electro-thermochemical are considered in two different scenarios. • Water scrubbing-based biogas upgrading system is used to supply input feed to a solid oxide fuel cell. • Opting for Cu-Cl cycle over PEM/CO 2 power cycle enhances economic and thermodynamic conditions. • Exergy, economic, optimization and uncertainty analyses are used to determine optimal conditions. The efficient and cost-effective design of an integrated structure relying on a biogas-based fuel cell for hydrogen (H 2) production can represent a key step to attain the goal of net-zero carbon emissions. In this paper, waste heat and generated power of the hybrid configuration based on the biogas purification cycle and solid oxide fuel cell are utilized to produce H 2. In the first H 2 production scenario, the waste heat and power are used in a copper-chlorine (Cu-Cl) thermochemical plant and a polymer electrolyte membrane (PEM) electrolysis unit. In the second H 2 production scenario, the power and waste heat are employed in a carbon dioxide power process and a PEM electrolyzer. Using an energy analysis, the thermal efficiency of the Cu-Cl/PEM-based system is 25.19% greater than that of the PEM-based system. The exergy efficiency of the Cu-Cl/PEM- and PEM-based systems are calculated at 48.10% and 40.12%, respectively. The prime cost of H 2 for the Cu-Cl/PEM-based system is 27.64% smaller compared to that of the PEM-based system. From the thermodynamic and economic perspectives, the first H 2 production scenario seems superior compared to the second one. Sensitivity analysis, machine learning, and multi-objective optimization approaches are used to extract the optimum conditions on the Pareto front. Biogas flow rate, cupric chloride change ratio, and inlet air to fuel cell as the inlet parameters and H 2 flow rate, net annual profit, and irreversibility as the output parameters in the machine learning analysis are considered. The parameters used in the machine learning analysis are obtained from the sensitivity analysis of the first scenario. Various decision criteria are employed to select the optimum operating conditions in the Pareto front. Using particle swarm optimization, the TOPSIS technique determines the operating conditions that have the highest H 2 production (i.e., 171.3 kg/h) and net annual profit (i.e., 3.407 MMUSD/yr). The fuzzy Bellman-Zadeh method introduces the conditions that have the lowest irreversibility for the first scenario. The uncertainty levels of the objective functions are examined using a Monte Carlo-based uncertainty quantification. [ABSTRACT FROM AUTHOR]

• Three deterministic tools are used to assess oil adsorption capacity on functionalized MNP. • The LSSVM-CSA model outperforms other AI/ML models. • The most effective input parameters are oil concentration and MNP dosage. • The GEP model gives a correlation for oil adsorption predictions with high accuracy. • This research provides tips/guidelines for effective design of oil emulsion/water separation. Using magnetic nanoparticles (MNPs) has emerged as a promising solution to capture oil from emulsified oily wastewater due to their high oil adsorption capacity, low toxicity, and reusability. Various factors affect the oil adsorption process using MNPs; thus, optimization of the process is required to achieve higher oil adsorption capacity. Smart models based on artificial intelligence (AI) are becoming popular as advanced computational tools to assess the non-linear relationships of variables in complex processes. In this study, least squares support vector machines (LSSVM) hybridized with the coupled simulated annealing (CSA) algorithm, adaptive network-based fuzzy inference system (ANFIS), and optimization techniques such as gene expression programming (GEP) are used to predict the oil adsorption capacity as a target variable. Oil concentration, mixing time, and MNP dosage as effective parameters are selected as input variables. After conducting experiments, 149 data points are obtained, 80 % of which is used in the training process and the remaining 20 % for the testing step. The performances of the developed models are evaluated using statistical parameters, including the coefficient of determination ( R 2), mean percentage error (MPE), and mean absolute percentage error (MAPE). According to the results, ANFIS and LSSVM-CSA models have a better performance than the GEP model in estimating the oil adsorption capacity with higher values of R 2 (>0.99) and smaller relative errors (close to zero) for all training, testing, and total datasets. Detailed model evaluation and error analysis indicate that the LSSVM-CSA model predicts slightly better than ANFIS with the highest R 2 of 0.9921 and a very small MAPE = 3.7597 % over the total dataset. Although the developed GEP model shows an acceptable prediction with R 2 > 0.95, the higher distribution of relative errors of the developed model results in a larger MAPE. Moreover, the GEP model computational time is considerably greater than that of the other models. The relative importance analysis using Pearson's and Spearman's correlation coefficients indicates that the oil concentration and MNP dosage are the most influential variables that affect the oil adsorption capacity. [ABSTRACT FROM AUTHOR]

• Promising chlorine- and sulfur-based thermochemical water-splitting cycles are investigated. • Challenges related to safe and efficient thermochemical cycles are discussed in detail. • Key factors for thermodynamic, economic, and environmental evaluation are classified. • Future prospects of research and engineering activities in this field are discussed. • Different strategies to reduce energy and economic/environmental parameters are recommended. Thermochemical water-splitting (TWS) processes are regarded as one of the most environmentally friendly strategies, capable of harnessing high-temperature waste heat from industries, renewable energy, and nuclear power sources to produce clean hydrogen (H 2) on a large scale. This review investigates and discusses the thermodynamic, economic, and environmental aspects of promising TWS cycles based on chlorine and sulfur. The TWS cycles discussed in this study include copper-chlorine (Cu-Cl), magnesium-chlorine (Mg-Cl), iron-chlorine (Fe-Cl), vanadium-chlorine (V-Cl), cobalt-chlorine (Co-Cl), cerium-chlorine (Ce-Cl), hybrid sulfur (HyS), sulfur-iodide (S-I), and zinc-sulfur-iodine (Zn-S-I) cycles. Challenges associated with the safe and efficient production of H 2 using the TWS cycles, operating conditions, and potential applications are recognized and discussed in detail. The economic, safety, and environmental aspects of the TWS cycles based on different energy sources (e.g., renewable and nuclear) are thoroughly examined. Enhancing the capacity of thermochemical cycles, employing pinch analysis, and utilizing cutting-edge optimization strategies based on state-of-the-art machine learning techniques and feasible optimization frameworks to reduce energy consumption can effectively reduce the TWS-based H 2 production costs. In a short-term strategy, the primary sources of energy supply for thermochemical cycles can be provided from current nuclear power plants, waste energy from various chemical industries, and renewable energy sources. Advanced reactors and artificial geothermal energy sources can be considered to power thermochemical cycles in a long-term strategy. The outcomes of the current work demonstrate that four-stage Cu-Cl units and four-stage Mg-Cl with dry hydrochloric acid absorption cycles in chlorine-based TWS systems, and S-I and HyS cycles in sulfur-based TWS systems are promising in terms of technical viability, efficiency, economic feasibility, and environmental compatibility. The energy efficiency of the Cu-Cl, Mg-Cl, S-I, and HyS cycles are reported as 32–58.60, 30–63.63, 15.30–60, and 35–48.80%, respectively. The economically optimal cycles for H 2 production include Cu-Cl (1.28–15.56), S-I (2.25–11.65), and HyS (2.97–9.98 $/kg). The global warming potential and acidification potential for the nuclear-based Cu-Cl thermochemical cycles are categorized as 0.50–1.35 kgCO 2 eq/kgH 2 and 1.75–9.60 kgSO 2 eq/kgH 2 , respectively; they are suitable candidates for H 2 production from the environmental perspectives. [ABSTRACT FROM AUTHOR]

• A critical review on asphaltene precipitation/deposition is provided. • Mechanisms, main characteristics, theoretical models, and pros and cons of asphaltene precipitation are discussed. • Parametric sensitivity analysis highlights vital factors/impacts in asphaltene precipitation. • Focal challenges for research/engineering activities in asphaltene precipitation are listed. • Useful guidelines to manage asphaltene deposition in petroleum industry are recommended. Asphaltene is the heaviest and most polar fraction of crude oils, which is usually defined as a solubility class, soluble in light aromatics, benzene and toluene, and insoluble in paraffins. Asphaltene precipitation/deposition negatively affects a variety of oil and gas processes, including oil recovery, transportation, and petroleum processing as it exhibits some undesirable impacts such as an increase in the viscosity of crudes and stability of the emulsions, a decrease in the distillate yields, and unstable phase separation. Experimental and theoretical investigations have revealed that asphaltene precipitation/deposition is influenced by several parameters/variables, including pressure, temperature, characteristics of the involved mixtures, properties and amount of precipitants, and characteristics of porous media. Knowing the undeniable consequences of asphaltene precipitation/deposition such as reduction of well productivity and/or blockage of the surface facilities, the effective treatments or inhibition techniques have gained widespread attention in the petroleum industry so far. Hence, a comprehensive knowledge of previous findings and an adequate understanding of different aspects of asphaltene precipitation/deposition (e.g., mechanisms and characterization) seem to be pivotal. Various attempts have been made to investigate different aspects of this phenomenon including thermodynamics of precipitation, kinetics of flocculation, and deposition mechanisms. The previous studies and importance of asphaltene in the oil and gas industry inspired us to provide a comprehensive review of the asphaltene precipitation/deposition that helps to further understand the governing mechanisms, thermodynamic behaviors, and transport phenomena prospects in this area. In this review work, the theories and mechanisms of precipitation/deposition, experimental and modelling approaches, the effects of operational parameters, and treatment/inhibition techniques are reviewed. Moreover, the economic analysis of flow assurance and the most common theoretical and practical challenges in asphaltene precipitation/deposition are studied. [ABSTRACT FROM AUTHOR]

• A stable and piston-like oil displacement is attained during carbonated water injection (CWI). • Gravity override and viscous fingering are not observed in CWI. • There is a critical injection rate for the maximum contact time between fluids. • The choice of well placement is strongly dependent on reservoir vertical and horizontal permeabilities. • CO 2 storage is an additional benefit upon conducting CWI. Carbonated water injection (CWI) has been well investigated to improve oil recovery when compared to other enhanced oil recovery (EOR) techniques both in the secondary and tertiary modes. Extra oil recovery and CO 2 sequestration associated with CWI have been studied through several experimental works. There are not adequate number of modelling studies about the CWI operation in the open sources because of the complex multi-physics involved with the fluid-fluid and fluid-rock interactions during CWI processes. Hence, further experimental and modelling investigations are needed to be conducted on CWI to systematically capture/comprehend the governing physics and complex displacement mechanisms. This research work will focus on the analysis of vital aspects such as oil recovery amount (and mechanisms), fluids distribution, and effects of operational parameters and well placement on the performance of CWI for both EOR and CO 2 sequestration purposes. To achieve these objectives, a 3-D heterogenous reservoir model is developed using the experimental data reported in the recent literature. A new approach of using the grid local pressure to model CWI is adopted where the moles of CO 2 /water are controlled by their injection rates. The dissolution of CO 2 in water is modeled by the Henry's law for each subsequent grid local pressure. In this research, it is found that through CWI, an additional oil recovery can be achieved when compared to plain (conventional) waterflooding (WF) in the secondary recovery mode. A subsequent increase in the injection pressure leads to more dissolution of CO 2 and enhancement of the overall performance of CWI. There is an optimum injector rate, which ensures an effective mass transfer across phases. An optimal well orientation will also give a better recovery performance during CWI. The amount of CO 2 stored is also illustrated in this work as an additional benefit achieved in the CWI processes. [ABSTRACT FROM AUTHOR]

• The gas hydrate dissociation and stability are assessed by MD simulation at molecular scale. • Key parameters (e.g., lattice parameter and density) of hydrate II are calculated with an acceptable accuracy. • The hydroxyl groups in alcohols cause instability and earlier decomposition in hydrate cages. • Inhibitors are ranked as follows: methanol > ethanol > glycerol in terms of inhibition effectiveness. • A parametric sensitivity analysis is conducted to make proper operating and policy decisions while dealing with gas hydrates. The present work aims to investigate stability and decomposition of hydrate structure II of methane, propane, and isobutane systems at various temperatures, pressures, and compositions in the absence and/or presence of inhibitor molecules. To assess the stability of gas hydrates, a comprehensive knowledge of the structural, thermodynamic, and dynamic properties of the hydrates is needed. The structure II of gas hydrates is embedded in a molecular dynamic (MD) approach and the simulations are carried out under constant temperature-constant volume (NVT) and constant temperature-constant pressure (NPT) conditions by employing the consistent valence force field (CVFF). In this work, first, the mean square displacement (MSD) and diffusion coefficient are evaluated to demonstrate the movement of host molecules. The radial displacement function is then utilized to display the characteristic configurations of structure II under different process and thermodynamic conditions. In addition, other vital properties including lattice parameter and potential energy are determined. The effect of inhibitors on stability and/or decomposition of hydrate structure II is investigated. The achieved results are in good agreement with previous theoretical and experimental outputs, confirming reliability and appropriateness of the simulation method. The inhibition capability of different inhibitors based on the simulation results has the following order: methanol > ethanol > glycerol. The findings of this study can help for better understanding of hydrate dissociation in molecular scale as well as for proper selection and design of effective inhibitors for hydrates with different structures and characteristics. [ABSTRACT FROM AUTHOR]

• Molecular dynamic simulations are conducted to investigate methane hydrate decomposition. • Stability of hydrate cages increases with increasing pressure and cage occupancy. • Adding an inhibitor to hydrate structure creates new hydrogen bonds, leading to a faster dissociation. • Good match between the real data and modeling outputs is attained. • A parametric sensitivity analysis is conducted to find the most influential factors affecting methane hydrate decomposition. Hydrate reserves play a crucial role in energy storage and resources across the world. Gas hydrate formation may lead to various forms of blockages in oil/gas production and transportation processes, resulting in high capital and operating costs. Hence, it is important to determine the methane hydrate formation conditions and to understand the vital process and thermodynamic parameters. In this study, molecular dynamic (MD) simulations are conducted to investigate the microscopic mechanisms/phenomena and intermolecular forces involved in methane hydrate decomposition, where molecular interactions, structures, and behaviours need to be appropriately determined/selected. Through a systematic parametric sensitivity analysis, the impacts of temperature, pressure, and cage occupancy on hydrate dissociation are studied. Furthermore, the diffusion coefficient, density, and heat capacity of the methane hydrates are determined by employing MD strategy. The stability of water cages is examined at various decomposition times, temperatures, and pressures. According to the radial distribution function and mean square displacement of oxygen-oxygen and carbon-carbon atoms, the stability of hydrate cages decreases with increasing temperature, while it increases with increasing the cage occupancy and pressure. The addition of inhibitors (e.g., methanol) to small cavities in the hydrate structure creates new hydrogen bonds between the water and inhibitor molecules in the cages, accelerating the decomposition of hydrates. A good agreement is noticed between the outcomes of this research work and the results obtained in experimental and theoretical studies available in the literature. Analysing the outcome of the present and previous research works, the current study provides new reliable/logical information on the molecular level of the hydrate dissociation process. It is expected that such a research investigation offers effective tips/guidelines to deal with hydrate formation and dissociation in terms of utilization, prevention, and processing. [ABSTRACT FROM AUTHOR]

This manuscript offers a thorough examination of the efficiency of a reactor-assisted membrane system for the production of syngas through a process of methane dry reforming (DRM). The proposed strategy aims to bring attention to the optimization of the DRM and reverse water gas shift (RWGS) reactions, towards achieving increased conversions of methane and carbon dioxide, as well as increased purity in syngas production. This goal will be accomplished by integrating a selectively permeable membrane within the central region of the tubular catalytic porous bed reactor. The primary objective of the current research is to quantitatively assess the impact of operational parameters on both the hydrogen flux and concentration distribution of the various components residing in the reactor. This study highlights the impact of inlet feed pressure on hydrogen flux. It is found that an elevation in the inlet feed pressure from 1 to 4 bar yields an average increase in hydrogen flux from 2.60 to 4.21 kg/(m2.h), particularly when the inlet feed pressure is set at 2 bar. However, a subsequent increase to attain a pressure of 4 bar results in a decline in hydrogen flux to a rate of 2.78 kg/(m2.h). Moreover, increasing the inlet feed temperature from 900 to 1100 K leads to a substantial augmentation in the average hydrogen flux, from 7.08 to 8.56 kg/(m2.h), while maintaining a reactor wall temperature of 1000 K. As another finding, a molar ratio of CH 4 /CO 2 of 1 leads to the greatest mean hydrogen flux of 4.73 kg/(m2.h) across the membrane interface. The current research work could provide useful tips/guidelines for efficient reactor design and optimization; it also enhances comprehension of reactor-assisted membrane systems in the context of syngas production. [Display omitted] • A novel reactor with a hydrogen-selective membrane enhances methane dry reforming. • The hydrogen flux is considerably influenced by operational conditions. • Increasing inlet feed pressure beyond 4 bar reduces the H 2 flux. • There is a good match between the simulation and experimental results. • An equimolar CH 4 /CO 2 ratio of 1 yields the highest H 2 flux, which is 4.73 kg/(m2.h). [ABSTRACT FROM AUTHOR]

The solar-driven photocatalytic process, as a clean and efficient advanced oxidation process (AOP), is widely operated in wastewater treatment applications. In the present work, a solar nano photocatalytic reactor is designed, manufactured, and tested under different operating conditions. The irradiation-dependence reaction kinetics is studied in a batch operation process. A comprehensive computational fluid dynamics (CFD) model taking into account realistic solar flux distribution is developed and validated using experimental data. Finally, the reactor scaling-up analysis for higher treatment capacities is performed with the aim of the design of industrial-scale solar photocatalytic reactors. The research results indicate that when the initial dye concentration is increased from 5 to 175 mg/L, the period required to achieve 95% pollutant removal in 20 L solution and 10 mg/L photocatalyst concentration boosts up to 2.2 times. When the photocatalyst concentration in the solution is increased from 5 to 20 mg/L, the time required to achieve 95% total removal is reduced to 35.7%. For the photocatalyst concentration of 75 mg/L compared to 20 mg/L, the time required for 95% degradation increases up to 16.7%. To treat 90% of the total pollutant in a 50 mg/L dye solution with an initial volume of 5 m3 and using 20 mg/L photocatalyst concentration under 11 h operation with an average solar irradiation intensity of 750.46 W/m2, a solar reactor with a surface area of 50.45 m2 is required, while for 10 m3 wastewater sample and the same conditions and 95% pollutant removal, the collector area is obtained to be 121.36 m2. A horizontal solar reactor compared to its optimal tilt angle can reduce the treatment capacity up to 55%. [Display omitted] • A solar-driven nano photocatalytic reactor for wastewater treatment was investigated. • The irradiation-dependent kinetics and CFD modelling of the reactor were performed. • A strategy was suggested to upscale the system and determine the reactor size for increased capacities. • Initial dye concentration and photocatalyst concentration affect treatment efficiency. • Collector tilt angle optimization is crucial for the highest performance. [ABSTRACT FROM AUTHOR]

Production from offshore reservoirs has contributed significantly to supply required hydrocarbons in the past several decades. The world's ever-growing energy demand has made the industry extend exploration and production activities to much deeper waters and harsher offshore environments. However, development of offshore reservoirs is a challenging task, and the role of efficient and reliable reservoir characterization tools in successful offshore developments cannot be overstated. Reservoir analysis in terms of determination of the properties of rock and fluids and their interactions constitutes a major part of reservoir characterization. Traditionally, laboratory tests and measurements are considered the most reliable characterization approach. However, in many cases, reservoir rock and fluid samples might be unavailable or insufficient for all the required tests and measurements. Empirical correlations and theoretical relationships can be employed in the absence of or to augment experimental data. However, characteristic predictions obtained from this deterministic strategy can be highly uncertain due to sensitivity to the property ranges, oversimplifying assumptions, and local variations of properties in heterogeneous reservoirs. Various forms of connectionist techniques have recently been used as an alternative method for property predictions with higher accuracy and reliability. Yet, their applications have been mostly limited to research studies rather than industrial projects. Recently, Molecular Dynamics (MD) simulations have gained considerable attention in studying reservoir rock and fluid properties, interactions, and associated phenomena at the atomic level. In MD, the properties of interest are extracted from the analysis of time evolution of atomic positions and velocities by numerical solution of Newtonian equations for the motion of all the atoms in the system. This technique facilitates performing "computer experiments" that may otherwise be impossible, extremely costly, or very dangerous to conduct. In this paper, the MD simulation technique and its applications to investigate rock and fluid properties are reviewed. The MD theoretical concepts and procedures are addressed, particularly in reservoir analysis. The main advantages and disadvantages of MD are listed. This review manuscript will provide useful guidelines to characterize the reservoir rock and fluid and their behaviours in various petroleum reserves, which considerably help to conduct better design and optimal operation of production plants. • A systematic review on molecular dynamic (MD) simulations in reservoir analysis is provided. • Theory and procedures of molecular dynamic simulations are adequately explained. • Pros and cons of MD strategy in fluid and rock characterization are discussed. • Useful guidelines for characterizing and modeling of petroleum reservoirs and transport phenomena using MD are recommended. • Current status and future aspects of MD in reservoir analysis are addressed. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • HPM and EHPM can have broad applications in chemical and petroleum engineering. • Multiple reaction systems and reactive absorption are modelled using new reliable tools. • HPM and EHPM offer simple, easy to use, and reliable solutions in reaction systems. • HPM solution exhibits high deviation from exact solution for complex differential equations. • EHPM provides more accurate modeling results than HPM for complicated chemical processes. Abstract There are a variety of chemical reactions and absorption equipment in chemical production plants across the world. Appropriate modelling of chemical reactors and reactive absorption systems is important for simulation, optimization, and scale-up purposes in the process/chemical engineering discipline. In this study, a chemical reactor and a reactive absorption system are modelled through different mathematical methods. The first case includes two unsteady state first-order reactions in series, and the latter system incorporates a first-order reaction and a diffusive mass transfer phenomenon. The concentration distribution for these two different cases is attained using Homotopy Perturbation Method (HPM) and Enhanced Homotopy Perturbation Method (EHPM) as an efficient, straightforward, and precise technique to solve differential equations. The primary approximation is generally selected with some unknown constant parameters, which can be obtained through applying the initial and boundary conditions. The solution is eventually attained in the form of power series. The modelling results obtained from HPM and EHPM are compared to the outputs attained from the available analytical solutions. The comparison implies that EHPM has a higher capability to provide proper solutions for the governing equations of both reaction cases in this study with a reasonable accuracy. EHPM appears suitable in several cases to obtain approximate analytical solutions where the governing transport phenomena equations in various chemical engineering processes lead to complicated differential equations. This research work introduces a systematic and efficient modelling procedure to properly capture important aspects such as reaction progress, concentration distribution, and phase change in complex transport phenomena systems. [ABSTRACT FROM AUTHOR]

Highlights • Chemical and energy processes demand advanced mathematical tools. • Hybrid methods are powerful in terms of computational burden, accuracy, and reliability. • MD and LBM help to further understand physics of processes at small scales. • This review provides helpful guidelines and procedures for various hybrid models. • This study offers proper screening criteria for various modeling approaches. Abstract Mathematical modeling and simulation methods are important tools in studying various processes in science and engineering. In the current review, we focus on the applications of hybrid models (HMs) in chemical processes, oil and gas processes, and applied energy systems. These processes suffer from complexities that demand advanced mathematical tools to be employed for various purposes such as process development, identification, simulation and modeling, optimization, control, classification, clustering, forecasting, and monitoring. Beside the lack of adequate knowledge about the processes related to the chemical and energy systems, there are other mathematical complexities such as non-linearity, large and multi-scale, long dynamics, uncertainties, and high dimensionality. The HMs can provide a practical solution to such complex processes. The arrangements of different black-box models with themselves or with white-box models are alternatively used to reduce the model complexities. The hybrid gray-box models are of significant importance as they combine the physical significance and generalization capabilities of the white-box models with the complexity reduction capability of the black-box models to facilitate/enhance the modeling strategy, while a desired precision is targeted. Such a hybridized model enables the physically-meaningful computation for the time-demanding applications. In this paper, we review different sub-models, hybridization strategies, structural designs, screening criteria, and new directions in hybrid modeling, with focus on the corresponding applications in chemical, petroleum, and energy systems. [ABSTRACT FROM AUTHOR]

In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery (EOR) process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine (LS-SVM) to calculate the CO2-oil swelling factor. A genetic algorithm is used to optimize hyperparameters (γ and σ2) of the LS-SVM model. This model showed a high coefficient of determination (R2 = 0.9953) and a low value for the mean-squared error (MSE = 0.0003) based on the available experimental data while estimating the CO2-oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2-oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2-oil swelling factor when adequate experimental data are not available. [ABSTRACT FROM AUTHOR]

In this study, an indirect charging/discharging solar-driven air heating system is assessed. In the charging process, a nano heat transfer fluid (NHTF) is thermally charged in a solar cylindrical cavity receiver (CCR) and then releases its thermal energy in a discharging process into the air in cross-contact with finned-tube heat exchangers (FTHEXs). In the experiments, a heating box with two consecutive FTHEXs is designed, and Syltherm 800 with Cu nanoparticles operates as the NHTF. The performance of the entire system in both single and dual FTHEXs modes is evaluated using various NHTFs, such as Therminol® 62, Therminol® 66, and Therminol® VP-1, and various nanoparticles, such as Ag, Cu, and Al 2 O 3. It is found that the dual HEXs mode has a better performance than a single HEX mode. Therminol® 66 has a better performance in the charging process in the CCR, and Therminol® 62 exhibits a better thermal performance in both the charging and discharging processes. The average daily thermal efficiency (ADTE) of the CCR and the entire system for considered operational ranges can be improved up to 65.79 and 39.25%, respectively, for a single HEX mode and up to 69.93 and 42.83%, respectively, for a dual HEXs mode. [Display omitted] • An indirect solar-driven air heater using nano heat transfer fluids (NHTFs) is studied. • Finned-tube heat exchangers (FTHEXs) are coupled to a parabolic dish collector (PDC). • The NHTF is thermally charged in a solar CCR and thermally discharged in FTHEXs. • A comprehensive multiphysics CFD model is developed for coupled PDC-FTHEXs. • Significant improvements in the system performance are achieved using NHTFs. [ABSTRACT FROM AUTHOR]

Storage and transfer of hydrogen (H 2) from production sites/plants to large-scale demanding industries are one of the most challenging obstacles to hydrogen adoption. Hydrogen liquefaction and its chemical storage in the form of ammonia (NH 3) can be used for safe and economic transportation. High energy consumption and carbon dioxide (CO 2) emissions to supply the utility of these systems can limit the production of liquid H 2 and liquid NH 3. Therefore, efficient design and practical strategies to store hydrogen at a large scale with minimal energy consumption and CO 2 emissions are necessary. In this research, an innovative hybrid system for portable hydrogen storage and power generation with zero CO 2 emission through liquefied natural gas (LNG) regasification is developed. The proposed structure includes a cryogenic air separation unit, magnesium–chloride hydrogen production process, ammonia production system, hydrogen liquefaction cycle, and Rankine/oxy-fuel power plants. The heat loss of the proposed structure is utilized to supply the required heat load for the thermo-electrochemical unit. The LNG regasification operation is utilized for pre-cooling the hydrogen liquefaction system and the necessary refrigeration of the air separation process and the final products. The developed structure is fed by 1883 kmol/h LNG, which is converted to 120 kmol/h liquid NH 3 , 180 kmol/h liquid H 2 , 924.5 liquid CO 2 , and 16.09 MW power. The thermal and exergy efficiencies for the designed system are 20.33% and 22.76%, respectively. The results of the exergy investigation reveal that the largest contribution of exergy destruction occurs in heat exchangers (31.27%), combustion chamber (34.65%), and turbines (8.12%). The economic evaluation indicates that the net annual profit, added value, and payback period are 15.93 US$/yr, 0.0779 US$/kWh, and 5.646 yr, respectively, based on the energy potential of Newfoundland and Labrador in Canada. Three-objective optimization approach using the combination of genetic algorithm and neural network exhibits that the energy efficiency and net annual profit based on the Bellman-Zadeh approach increase up to 27.04% and 45.20 US$/MMBTU, respectively, and the irreversibility reduces to 99.4 MW. [Display omitted] • A novel structure for production of power, liquid H 2 , liquid NH 3 , and liquid CO 2 is developed. • The LNG regasification is used in hydrogen liquefaction and air separation systems. • The waste heat of the oxy-fuel power plant is recovered in Mg–Cl unit for hydrogen production. • The system exergy efficiency and payback period are 22.76% and 5.646 yr, respectively. • A combination of the GA and AI is used for optimization. [ABSTRACT FROM AUTHOR]

Recently, considerable attention has been given to using magnetic nanoparticles (MNPs) for capturing oil from oil-in-water (O/W) emulsions, despite MNPs' inherent instability and agglomeration. Their stabilization through changing surface chemistry is required to increase dispersivity. In this research, we use cetyltrimethylammonium bromide (CTAB) as a cationic surfactant to increase the positive surface charge of the particles, resulting in a better stability in the aqueous solution because of increased repulsive forces. The functionalized MNPs are characterized using transmission electron microscopy (TEM), zeta potential, and contact angle (CA) measurements. The aim of this study is to investigate the oil separation efficiency (SE) and equilibrium oil adsorption capacity of the synthesized particles, which are determined using gas chromatography analysis. We also study the adsorption behavior using isotherm and kinetic models. The SE values indicate the superior performance of MNP@CTAB for oil adsorption from dodecane-in-water nanoemulsion (SE = 99.80%) compared to the bare MNPs with SE of approximately 57.46%. These findings are attributed to the stronger electrostatic attraction between the MNP@CTAB having high positive charge and negatively charged oil droplets. The adsorption isotherm results using both linear and non-linear regression methods show that the Freundlich isotherm is the best fit to the experimental equilibrium data (with calculated R2 > 0.97), verifying a multilayer heterogeneous adsorption. Moreover, the pseudo-first-order kinetic model describes the experimental equilibrium data in a greater congruence (R2 = 0.99), suggesting physical adsorption of oil onto MNPs through van der Waals and physical bonding, which is also confirmed through zeta potential measurements. [ABSTRACT FROM AUTHOR]

Catalyst deactivation is one of the main challenges in industrial reaction operations. In this paper, a dynamic heterogeneous model is developed for the crude terephthalic acid hydropurification process. The model incorporates the effects of axial mixing and deactivation of the commercial catalyst of palladium supported on carbon on efficiency of the hydropurification operation. The transport phenomena governing equations lead to a series of partial differential equations which are simultaneously solved. The model validation is accomplished using the industrial data. The proposed model satisfactorily simulates the real process, and it is more accurate than the plug flow model to forecast the process behaviors. A decline in the catalyst particle size improves the catalyst performance; an increase in the catalyst porosity prolongs the catalyst lifetime, while maintaining an acceptable pressure drop in the catalyst bed. It is concluded that the effective control of p-xylene oxidation reactor (in terms of process conditions) and inlet temperature rise lead to more efficient purification process [ABSTRACT FROM AUTHOR]

The CO 2 based enhanced oil recovery methods (EORs) in the petroleum industry are considered as one of the efficient technologies for further production where the natural driving forces become weak. To determine which EOR method is more appropriate for a targeted reservoir, there is a need to develop a reliable and fast tool to predict the performance of the EOR methods due to assumptions and central processing time (CPU) time of reservoir simulations. We develop a promising approach for predicting the ultimate oil recovery factor of the miscible CO 2 injection processes. To attain this goal, the least square support vector machine is used to build the proxy model. The Box-Behnken design as a branch of response surface methods is employed to design simulation runs for miscible CO 2 injection processes, and the leverage method is applied to validate the proxy model in terms of statistical perspective. An artificial heterogeneous reservoir is used to perform compositional reservoir simulations. Five operational parameters of the miscible CO 2 injection process are considered, including bottom-hole flowing pressure (BHP) of injection well (psi), CO 2 injection rate (MMSCF/D), injected CO 2 concentration (mole fraction), bottom-hole flowing pressure (BHP) of production well (psi), and oil production rate (STB/D). The developed proxy model can be employed to forecast the ultimate oil recovery factor of the miscible CO 2 injection operations at the different rock, fluids, and process conditions. The proposed method appears to be an efficient simulation strategy that offers guidelines and screening criteria for the application of the miscible CO 2 injection. [ABSTRACT FROM AUTHOR]

Formations with interconnected pathways of fractures (or high permeability regions) between the production and injection wells suffer from early breakthrough and low cumulative recovery during gas injection process due to the contrast in the capillary pressure of matrix and fractures. A methodology is developed to modify the production well by a high breakthrough capillary pressure skin and by controlling the injection pressure. Consequently, the liquid recovery at the gas breakthrough is significantly enhanced. This flow manipulation prevents the gas pressure to exceed the breakthrough capillary pressure plus pressure drop of the skin. The experiments are conducted in a sintered heterogeneous glass bead model using air-liquid (e.g., water and CMC solutions) systems. Tests are initially performed without the pressure control to obtain the injection pressure at which the gas breakthrough occurs. In the experiments, the gas injection flow rate is reduced to half successively, whenever the gas injection pressure reaches within 10% of the skin breakthrough capillary pressure. We continuously measure the cumulative liquid production weight and the gas injection pressure over time; pictures are continuously captured from the process to track the advancement of air-liquid interface in matrix, fracture, and skin. The effects of flow rate, drainage direction (vertical or horizontal), and fluid viscosity on the process performance are investigated. Without the use of a skin and pressure control, the recovery at breakthrough from the horizontal model is only limited to that from the fracture which is about 9% of the pore volume. The overall recovery increases to more than 90% in the presence of the skin and injection pressure control. The recovery in the vertical gas injection benefits from additional driving force provided by gravity; the recovery factors (RF) as high as 93% are achieved in the vertical drainage tests using the skin, and even without the pressure control. The proposed methodology is successfully tested, implying its promising features such as delayed gas breakthrough in a highly heterogeneous porous medium where fractures interconnect the injection and production wells. This method has potential applications in enhanced oil recovery, remediation of contaminated porous media, and membrane separation processes. [ABSTRACT FROM AUTHOR]

Determination of equilibrium composition for various multi-phase systems is important in the context of thermodynamics. Three methods are generally employed to calculate the gas/liquid equilibrium compositions; namely, empirical graphs, correlations, and equations of state (EOSs). Empirical graphs and correlations are simple and fast in terms of calculation procedure. Furthermore, using an EOS requires an initial guess, which is usually obtained via empirical correlations. In this study, the gas-oil composition of 10 different crude oils (20–40 °API) are experimentally determined by a gas chromatography (GC) apparatus within a temperature range of 600–1212 °R and a pressure range of 14.7–7000 psi. A robust predictive model is then proposed to estimate the equilibrium ratios (K i ) of hydrocarbons and non-hydrocarbons. This model is generated by utilizing the least squares support vector machine (LSSVM), while genetic algorithm (GA) is used for selection and optimization of hyper parameters (γ and σ 2 ) that are embedded in the LSSVM model. The coefficient of determination (R 2 ) for the introduced model is 0.9991 and 0.9979 and the mean squared error (MSE) is 0.00074 and 0.044 for the hydrocarbons and non-hydrocarbons, respectively. The proposed model is simple to use and exhibits high accuracy and reliability, which can have various applications in chemical and petroleum industries where the thermodynamic equilibrium is maintained. [ABSTRACT FROM AUTHOR]

Minimum miscibility pressure (MMP) is one of the key parameters that affects the microscopic and macroscopic effectiveness (displacement performance) of gas injection for enhanced oil recovery. Numerous research efforts have been made to measure and predict the MMP, including experimental, analytical, numerical, and empirical methodologies. Despite these efforts, a comprehensive, user-friendly, and accurate model does not exist yet. In this study, we introduce “Gene Expression Programming (GEP)” as a novel connectionist tool to determine the MMP parameter. This new model is developed and tested using a large databank available in the literature for the MMP measurements. The accuracy of the proposed model is validated and compared with the outcomes from the commercial simulators. The performance of the proposed model is also examined through a systematic parametric sensitivity analysis where various input variables such as temperature and volatile-to-intermediate ratio are considered. The new GEP model outperforms all the published correlations in term of accuracy and reliability. [ABSTRACT FROM AUTHOR]

Hydrate reservoirs have steadily emerged as an important contributor in energy storage. To better understand the role of hydrates in gas production, it is vital to know the challenges related to the hydrate dissociation. To highlight the main technical challenges, further research and engineering investigations are needed for interactions between the molecules, phase behaviours, and detailed mechanisms of hydrate formation and dissociation. This review paper describes the gas hydrate reservoirs, hydrate dissociation, and previous research works related to gas engineering. This study briefly presents the key theoretical concepts and drawbacks of different techniques/kinetics of decomposition; consisting of depressurising, thermal stimulation, chemical injection, and gas swapping. This will be followed by the theory on the molecular dynamics simulation and its application in various decomposition methods. Owing to the limitations of existing experimental and theoretical approaches, development of more accurate theoretical models and equations of state (EOSs) is inevitable. The molecular dynamics simulation strategy has been used as a strong research tool with adequately small scales in both space and time. The practical implication of molecular dynamics (MD) simulation in hydrate dissociation methods is illustrated at the end of this study for further clarification. The complex nature of hydrates clearly implies that new potential functions for current MD tools are required to satisfactorily comprehend the hydrate molecular structure and mechanisms of hydrate decomposition. [ABSTRACT FROM AUTHOR]

Brown algae adsorb a considerable amount of CO 2 and store carbon in their biomass more than many other algae species. Nowadays, depletion of fossil fuel resources, greenhouse gas emissions, climate change, and the future of human food security have encouraged scientists and policymakers to develop safer and more sustainable alternatives of energy sources. Brown algae have attracted most of this attention in North American countries to generate a wide range of products and biomass for biorefinery. In this review paper, various aspects of brown algae cultivation and its biorefinery process are discussed. Main drivers of the sectors, opportunities, challenges, and future prospects of brown algae-based biofuels in North America have been taken into account; the most appropriate processes and pathways are compared to maximize the outputs and environmental benefits, and minimize costs and drawbacks of the industry. Analysis and predictions of renewable energy future in North American countries revealed that biomass-derived fuels, including brown algae biofuels, will play an important role especially in the transportation sector. These biofuels, including bioethanol and biodiesel are accompanied by various co-products and by-products in an optimized biorefinery system to convert every available component of brown algae. However, cultivation of brown algae to produce biomass at a commercial scale with minimum costs and market demands are the main challenges of algal biofuels. Finally, expanding the value chain of the brown algae biorefinery by producing more advanced biofuels such as aviation fuels and value-added co- and by-products would significantly impact North American countries' gross domestic product. • Many studies related to biofuel production from brown algae are reviewed comprehensively. • Brown algae have attracted significant attention in North America. • Costs and markets demands are two main challenges for this algal energy. • Effective strategies for optimal production of biofuel from brown algae are provided. • Current challenges and future prospects for biofuel from brown algae are discussed. [ABSTRACT FROM AUTHOR]

Slow drilling in deep shale formations leads to a considerable expenditure to the petroleum industry. An important factor that contributes to slow penetration rate is bit balling in water-reactive shale formations. Bit balling is recognized as the major reason for inefficient performance of the bit while drilling shaly formations. The corresponding research centers and industry are always interested in finding practical solutions to mitigate bit balling associated with slow shale drilling. In this study, three well-performing and robust pattern recognition techniques including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and neural network pattern recognition (NNPR) are presented for problem identification in drilling engineering. Each method constructs three structures considering important inputs: normalized rate of penetration, depth of cut, specific energy, and cation exchange capacity to diagnose effective bit cleaning in shale formations of the Southern Oil Field in Iran. The models correlate operational parameters to cation exchange capacity to determine whether effective bit cleaning (reversal of balling) or ineffective cleaning (irreversible balling) is taking place. The common evaluation approaches include cross penalty error, confusion matrix output, area under the curve (AUC), and receiver operating characteristic (ROC) to evaluate efficiency of the multi-strategy classifier. These indicators provide useful information on the number of classified and misclassified cases, global accuracy, and discriminatory ability of diagnostic tests. We show that pattern recognition methods can assure both stability and high accuracy in classification situations. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel solar-driven propane PBCR for hydrogen production is investigated. • An HTF is thermally charged in a solar PDC and provides the energy needed for PBCR. • A comprehensive multiphysics CFD model is developed for coupled PDC-PBCR systems. • The daily performance of the PBCR affected by several key parameters is studied. • The propane conversion during a typical sunny day can be obtained up to 62.93%. In this study, a solar-driven porous bed catalytic reactor (PBCR) is investigated with the aim of propane reforming and hydrogen production using a clean energy source. The process consists of two major parts: a PBCR equipped with internal heating tubes and a parabolic dish collector (PDC) with its cylindrical cavity receiver (CCR) to supply the thermal energy required for the reforming process. A proper heat transfer fluid (HTF) is thermally charged in the CCR and discharged in the PBCR. In addition to the reforming reaction, the reversible water–gas shift (WGS) reaction takes place in the PBCR. In the reverse WGS, hydrogen produced in the previous stage is consumed and undesired carbon monoxide is generated. This unfavorable condition should be controlled to restrict the production of undesirable byproducts. In the present study, two CCR and PBCR parts are modeled/simulated using the CFD approach, and then validated using available experimental data. After model validation, the impacts of critical parameters such as inlet gas temperature, pressure and composition, bed porosity, PDC aperture diameter, and HTF circulation rate within the system on the performance of the entire system are assessed. Propane conversion, hydrogen yield, and carbon monoxide selectivity (CMS) are chosen as the system's performance indicators. At optimal conditions, for an inlet pressure of 100 Pa and temperature of 300 K, inlet propane mass fraction of 0.05, bed porosity of 0.3, PDC aperture diameter of 3 m, and HTF circulation rate of 0.05 kg/s, the time average propane conversion, hydrogen yield, and CMS during a typical day and a part of nighttime are obtained at about 62.93 %, 10.326, and 1.0122 %, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel system for production and storage of H 2 with net-zero carbon emissions is developed. • High-temperature fuel cells and biogas upgrading unit are efficiently integrated. • Waste heat of the fuel cells is recovered in the Cu-Cl unit for hydrogen production. • Integrated process can generate 108 kmol/h liquid hydrogen and 18.88 MW power. • A hybrid method of the GA and artificial intelligence tools is applied for optimization. Global energy consumption has been exponentially increasing, leading to the depletion of conventional energy sources and a considerable increase in greenhouse gas emissions. The exploitation of sustainable energy sources and the development of innovative methods for mitigation of CO 2 emissions is necessary. Hydrogen (H 2) as a versatile and green fuel can be produced and stored in liquid form using sustainable energy sources through innovative methods. In this paper, a novel optimal hybrid structure is proposed for hydrogen production and storage with net-zero CO 2 emissions. A high-temperature fuel cell is utilized to reduce CO 2 emissions and supply the heat and power needed to produce and liquefy hydrogen. The hybrid configuration employs an electro-thermochemical unit for hydrogen production, molten carbonate fuel cells, a hydrogen liquefaction process, and a biogas upgrading by water scrubbing. A robust combination of meta-heuristic algorithms and artificial intelligence is developed to optimize the investment return period and energy/exergy efficiencies. The fuzzy Bellman-Zadeh, LINMAP, and TOPSIS techniques are also used to make decisions in multi-objective optimization. This innovative configuration generates 108 kmol/h liquid hydrogen, 18.88 MW power, 54 kmol/h oxygen, and 3258 kmol/h hot water. Pinch, sensitivity, exergy, and economic assessments are implemented to evaluate the performance of the hybrid structure. The thermal and exergy efficiencies of the proposed configuration are obtained to be 78.21 % and 64.83 %, respectively. The greatest portions of destructed exergy are resulted from the fuel cells-gas turbine hybrid system (63.63 %), electro-thermochemical unit (25.96 %), and biogas upgrading technology (6.23 %). The returns period on investment, the prime price of electricity, and the net benefit are computed as 4.854 years, 0.0420 US$/kWh, and 15.73 million US$/years, respectively, based on the energy potentials of Newfoundland and Labrador, Canada. [ABSTRACT FROM AUTHOR]

[Display omitted] • The current status and future prospects of hydrogen production and utilization are discussed. • Challenges and government regulations for hydrogen production in North America are addressed. • High energy consumption and CO 2 emissions are the main challenges for conventional H 2 production. • Hydrogen storage materials are still lacking in both volumetric and gravimetric density. • Biohydrogen production needs further research and development before commercialization. Hydrogen is one of the most efficient and attractive energy carriers that can fulfill current and future energy requirements and address the drawbacks of conventional energy resources. Hydrogen as a fuel is nonmetallic, carbon-free, non-toxic, and has higher specific energy than gasoline (on a mass basis). Hydrogen production, storage, safety, and utilization are the four main aspects that should be considered in hydrogen energy-based systems. This review extensively analyzes the literature on fundamental, technological, and environmental aspects of various hydrogen applications and production techniques as well as theoretical and practical challenges. The global demand for hydrogen is mainly for its utilization in oil refineries (33%), chemical production (40%), metallurgical industries (3%), and the rest is in applications such as fuel, glass manufacturing, welding processes, and food industries. Natural gas, crude oil, coal, and electrolysis processes are the main feedstock sources for hydrogen production, with shares of 49, 29, 18, and 4%, respectively. Currently, hydrocarbon and alcohol reforming, gasification (coal and fossil fuels), and fossil fuel partial oxidation have the greatest shares of the hydrogen production methods. The main challenges for these methods are the high total energy consumption and carbon emissions to the environment. Water electrolysis technologies are still under development and can be combined with renewable energy resources, such as solar, geothermal, wind, and tidal to achieve eco-friendly technologies. Biohydrogen production through biological approaches such as direct and indirect biophotolysis, and photo and dark fermentation processes can be sustainable and promising technologies for bioenergy production worldwide. This review investigates the various thermochemical cycles for hydrogen production and presents the process flow diagram of each cycle. It discusses the different chemical and physical methods and materials for hydrogen storage. In fact, hydrogen storage materials are still lacking in both volumetric and gravimetric density, and in some cases, they do not have promising storage capacity. The current status of hydrogen production, available resources, various challenges in the field of hydrogen production, storage and transportation, and government regulations in North America are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vital parameters affecting control and utilization of co-culture systems are evaluated. • Utilization of microalgal microbial fuel cells in wastewater treatment is reviewed. • Microalgal co-cultivation systems for biomass/biofuel production are analyzed. • Techniques for biomass and lipid content measurement are comprehensively discussed. • Current challenges and future prospects in microalgal co-cultures progress are addressed. The adverse impacts of fossil fuels on the environment, specifically climate change, have intensified the need for finding a sustainable alternative source of energy. Numerous studies have postulated that biotechnology development, focusing on biofuel production processes, could be a suitable solution for sustainable energy production. The utilization of microorganisms, such as microalgae, is one of the basic strategies to produce biodiesel, pharmaceuticals, and nutraceuticals. Co-cultivation has overtaken mono-cultivation to improve the production of microalgae due to its endurance, foreseeability, and stability. However, further development of the co-cultivation process requires elaborate efforts to make it safe, practical, and optimal. Some dominant factors affecting the co-culture system control are the diversity of the cell groups, mass transfer, scale-up, population ratio, and time. In this review article, we will discuss some critical topics related to the co-cultivation process, such as data collection, modelling, cultivation methods, and interaction varieties. An overview of the quantification techniques for biomass concentration and lipid content is also provided. Moreover, the utilization of microalgal co-cultures will be analyzed, depicting the difficulties associated with their efficient control. As knowledge of the reactions and their entailing kinetics is elemental for analyzing the microalgal systems, which are used to synthesize intermediate products and chemicals, some studies focusing on the kinetics of microalgal biomass conversion into biofuels are presented. Since the microbial fuel cells (MFCs), as a new bioelectrochemical process, are utilized in the mixed-culture systems to treat wastewater and produce biofuels and other valuable by-products, a summary of the microalgal MFCs is also provided. Finally, arguments about the challenges and advantages of the co-cultivation systems are presented. [ABSTRACT FROM AUTHOR]

Ice gouging is one of the critical threats to the subsea pipelines crossing the Arctic and neighboring shallow waters. The burial of subsea pipelines is considered a viable solution to protect them against ice gouging attacks. The pipeline is usually buried below the deepest recorded ice gouges in that specific geographical location but is still threatened by subgouge soil deformation that is extended down the ice tip due to the shear resistance of the seabed soil. Determination of the subgouge soil deformations is a challenging design aspect that usually requires costly experimental and numerical studies to ensure the structural integrity of the buried pipeline against ice gouging. In this paper, an alternative and cost-effective methodology has been proposed using the group method of data handling (GMDH) to simulate the horizontal and vertical subgouge soil deformation profiles in the sandy seabed. Ten GMDH models (GMDH 1 to GMDH 10) were defined by using the governing input parameters comprising the soil depth ratio, the gouge depth ratio, the shear strength of seabed soil, the attack angle, the frontal berm height ratio, the horizontal and vertical loads, and the ice dynamics. The results from the best GMDH models were compared with the artificial neural network and empirical approaches, which showed a robust performance. [ABSTRACT FROM AUTHOR]

Asphaltene deposition is a recognized phenomenon in petroleum industry with undesirable outcomes so that it may lead to wellbore plugging and formation damage, resulting in a large amount of remedial costs to decrease its negative impacts on oil production. Therefore, it has attracted lots of research interests in the literature. In this study, an attempt is made to introduce the least square support vector machine (LSSVM) for prediction of asphaltene deposition. This technique with high capabilities which captures the complex nature of asphaltene could be inferred as a scaling model. As there is no a standard procedure to determine the main parameters of the LSSVM model, the particle swarm optimization (PSO) technique is employed to synchronously optimize the LSSVM parameters. The modeling results clearly demonstrate that the optimized LSSVM is able to handle the nonlinearities well and attain satisfactory results. The comparison of available predictive equations for asphaltene deposition confirms that the LSSVM technique linked with PSO exhibits higher robustness and greater precision with an R 2 of 0.989 for the testing phase. [ABSTRACT FROM AUTHOR]

Adequate Knowledge of reservoir fluid characteristics (e.g., bubble point pressure) plays a crucial role while conducting modeling/simulation of production processes in petroleum reservoirs. Although many efforts have been made to obtain proper correlations for prediction of bubble point pressure (BPP) of reservoir fluids, there is still relatively high magnitude of error with the developed predictive tools available in the literature. To fill this lacuna, a robust and effective technique, called gene expression programming (GEP), is employed to determine BPP of crude oil samples as a function of temperature, oil composition, molecular weight of C 7+ , and specific gravity of C 7+ . The GEP method is built based on the experimental (or real) data used for training and testing phases in order to develop an appropriate correlation. The previous predictive methods are also reported in this study and employed to calculate BPP as a function of independent parameters when the same data bank is utilized. Comparing the outputs obtained from the previous models with the BPP values predicted by the GEP technique, it was found that the GEP approach exhibits higher accuracy and lower uncertainty on the basis of statistical analysis in terms of coefficient of determination ( R 2 ) and mean squared error (MSE). Great precision attained in this study through using GEP recommends linking reservoir simulator packages with the GEP tool when thermodynamic properties such as BPP are required for modeling and optimization purposes. [ABSTRACT FROM AUTHOR]

One of the most important factors affecting pavement performance is climate, including frost action and precipitation. The performance of pavements in frost-affected regions depends to a large degree on the depth of frost penetration. In this paper, a simple predictive tool is developed to calculate a new correction coefficient depending upon the thermal ratio and fusion parameter. The new correction coefficient can be used in follow-up calculations to estimate the depth of frost penetration for both uniform and layered soils in frost-affected regions to evaluate the performance of pavement. The results of the proposed method are found to be in excellent agreement with reported data in the literature with average absolute deviation being less than 0.8%. The predictive tool is simple, straightforward and can be readily implemented in any standard spreadsheet programme leading to accurate, smooth and non-oscillatory data points. The prime application of the method is as a quick-and-easy evaluation tool in conceptual development and scoping studies in which the depth of frost penetration for both uniform and layered soils in frost-affected regions is being considered. The method may also serve as a benchmark in numerical and rigorous simulation studies. [ABSTRACT FROM PUBLISHER]

• Induction time of of methane hydrate is estimated using powerful smart tools. • The sensitivity analysis helps to obtain the optimal inhibition conditions. • The GEP tool has a better predictive performance among the models. • Temperature and subcooling are the most important parameters affecting induction time. • A good match is noticed between the predictions and real data. The formation of gas hydrates in offshore pipelines is a severe flow assurance issue. The hydrates may form quickly in pipelines without any warning. Thus, effective remediation approaches may require to be performed for days or months. Injecting chemicals such as kinetic hydrate inhibitors (KHIs) can be implemented to prevent or manage gas hydrate formation in offshore facilities. KHIs with polymeric structures and various functional groups postpone hydrate nucleation and growth with a subcooling of 8–10 °C. In this paper, a new approach is introduced to predict the induction time of methane hydrate formation in the presence of Luvicap 55 W solutions (as a KHI) with a broad range of concentrations. In the intelligent models, including least squares support vector machine (LSSVM), adaptive network-based fuzzy inference system (ANFIS), and gene expression programming (GEP), are employed in this study, where 440 experimental data are collected. In the intelligent modeling, 85% of data are utilized for the training step and 15% for the testing step. The smart tools relate the induction time parameter as a target to input parameters such as the molecular weight of solution, mass fraction of KHI, temperature, pressure, and subcooling. The key statistical parameters, including average relative error percent (ARE %), average absolute relative error (AARE %), and coefficient of determination (R2), are calculated to evaluate the performance of the deterministic models. The values of the coefficient of determination (R2) for the developed GEP model are 0.9582 and 0.9726 in the training and testing steps, respectively. The GEP model exhibits the best performance for induction time estimation compared with the LSSVM and ANFIS models, though the run/computational time of this model (e.g., 18 min) is considerably greater than that of other deterministic approaches. Using the ANFIS model, the most influential parameters affecting the induction time are the system pressure and temperature with direct relationship. In addition, other input parameters, including the molecular weight of the solution, mass fraction of the Luvicap 55 W, and subcooling, exhibit indirect relationship with the induction time. [ABSTRACT FROM AUTHOR]

Thispaper presents an experimental investigation of gas-assistedgravity drainage (GAGD) performance while taking into account theeffects of asphaltene deposition. The experiments were conducted athigh pressures and high temperature (15–37 MPa and 102 °C)using carbonate cores and real reservoir fluids in the absence ofwater saturation. An 85% methane-enriched hydrocarbon gas mixturewas employed as an injection fluid in the laboratory runs. The finalrecovery factor for a fairly tall core was 52% of the original oilat immiscible conditions during hydrocarbon gas injection when theoperating pressure and the amount of injected gas were 28.3 MPa and1.2 pore volume, respectively. In all of the experiments, the totalamount of asphaltene deposition was less than 2 wt % of the originaloil and no considerable reduction in permeability was found. A numericaltwo-phase (gas–oil) simulator coupled with a deposition modelwas also developed to evaluate the importance of different parameterscontributing to the final recovery. There was a good agreement betweenthe modeling and experimental results, showing an average error percentagelower than 5%. This study can aid the prediction of the performanceof gas injection processes experiencing asphaltene deposition andalso aid in proper design of gravity drainage-assisted enhanced oilrecovery methods. [ABSTRACT FROM AUTHOR]

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation. [ABSTRACT FROM AUTHOR]

To calculate the radiant intensity at a point distant from a radiator, a geometrical or configuration factor must be used. If the temperature is known, then the emissive power can be calculated using the mean equivalent beam length, but before the radiant heat flux at a distance can be estimated, a configuration factor must be calculated. In this article a simple predictive tool is developed to estimate the configuration factor for a geometry including a receiving element lying on the perpendicular to one corner of a radiant rectangle. The results are found to be in excellent agreement with reported data in the literature with average absolute deviation being around 2%. The results can be used in follow up calculations to estimate heat fluxes on surfaces exposed to radiation. The tool developed in this study can be of immense practical value for engineers as a quick check on the configuration factor for a geometry including a receiving element lying on the perpendicular to one corner of a radiant rectangle without opting for any experimental trials. In particular, engineers would find the approach to be user-friendly with transparent calculations involving no complex expressions. [ABSTRACT FROM PUBLISHER]

Steam-assistedgravity drainage (SAGD) is an enhanced oil recoverytechnology for heavy (or viscous) oil and bitumen that involves drillingtwo horizontal wells in underground formations. Laboratory work, pilot-plantstudies, and mathematical model development, which are generally costly,difficult, and time-consuming tasks, are taken into account as importantstages in finding an effective and economical method and also predictingthe performance of the SAGD technique for a certain heavy-oil reservoir.Currently, smart techniques as accurate and fairly fast tools arehighly recommended for these purposes. In this work, an experimentalstudy and an artificial neural network (ANN) linked to an optimizationtechnique, called particle swarm optimization (PSO), were employedto obtain performance parameters such as the cumulative steam-to-oilratio (CSOR) and recovery factor (RF) for the SAGD process. The outputsof the developed connectionist modeling (i.e., ANN–PSO) werecompared with actual data, showing an average error lower than 7%,mostly because of the supremacy of the ANN–PSO method comparedto the conventional ANN method and the correlations developed in thisstudy. Furthermore, it is concluded that, among the contributing parameters,reservoir thickness and oil saturation have the most significant impactson RF and CSOR during SAGD operations. The current study confirmsthe potential of hybrid connectionist modeling to screen heavy-oilfractured reservoirs for the SAGD process. [ABSTRACT FROM AUTHOR]

Highlights: [•] Compressibility of gas was predicted by a machine learning-based method. [•] Least square support vector machine (LSSVM) was used for modeling. [•] The optimal values were tuned by coupled simulated annealing optimization. [•] The performance evaluation yielded R 2 and MSE of 0.999 and 0.000014, respectively. [•] The inputs constitute of pseudo pressure and temperature of natural gas. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensionalmathematical model forsteamflooding in heavy oil saturated naturally fractured carbonatereservoirs where the governing equations are written for the matrixand fracture media, separately. Uncertainties associated with theshape factor for the communication between the matrix and fracturewas eliminated through setting a continuity boundary condition atthe interface. Using this boundary condition, the solution methodemployed differs from the most of the modeling simulations reportedin the literature. The Newton–Raphson approach was also usedfor solving mass and energy balance equations. The recovery factorand cumulative steam to oil ratio were obtained as a function of steaminjection rate, temperature, and also characteristics of the fracturedmedia such as matrix size and permeability. The numerical solutionclearly shows that fractures play an important role in improving theconduction of heat into the matrix part. It is also concluded thatthe matrix size and total permeability are the most important parametersaffecting the dependent variables involved in steamflooding in thistype of reservoir. The results obtained from the mathematical modelingwere compared with the real data, exhibiting a reasonable agreement. [ABSTRACT FROM AUTHOR]

Calcium carbonate, or calcite, scale is frequently encountered in oilfield operations. In a reservoir, when brine is produced, it is disturbed because the brine is moved to lower temperature and pressure areas. The dissolved concentration of calcium carbonate is greatly influenced by temperature and the partial pressure of carbon dioxide gas over the water. The net result of a drop in temperature and pressure may therefore be an increase or a decrease in the calcium carbonate saturation ratio as determined by the temperature change relative to the pressure change. In this article, a simple Arrhenius-type function is developed to estimate the combined effect of temperature and carbon dioxide pressure on calcium carbonate dissolved concentration in oilfield brines. Estimations are found to be in excellent agreement with reported data in the literature with average absolute deviation being less than 2%. The tool developed in this study can be of immense practical value for engineers to have a quick check of combined effect of temperature and carbon dioxide pressure on calcium carbonate dissolved concentration at various conditions without opting for any field or experimental trials. In particular, engineers would find the approach to be user friendly with transparent calculations involving no complex expressions. [ABSTRACT FROM AUTHOR]

Asphaltene precipitation (AP) is recognized as a complicated occurrence that results in weakening reservoir characteristics and subsequent considerable decline in oil production rate. Asphaltene instability occurs due to variations in thermodynamic properties such pressure, temperature, and mixture composition. AP prediction is an important design factor in implementation of any enhanced oil recovery (EOR) process. In this study, experiments were conducted using some light oil samples to measure important phase behavior properties such as the bubble point pressure (BPP) and the amount of precipitated asphaltenes. A thermodynamics model was also developed to determine equilibrium compositions of the oil samples, considering AP. Then, potential application of a feed-forward artificial neural network (ANN) model, optimized by the imperialist competitive algorithm (ICA), was proposed to estimate BPP and the amount of AP. Comparison between the ICA-ANN predictions and the experimental data shows that the average absolute error between data originated from these two different approaches is less than 5%. In addition, it was found that temperature and pressure have the greatest impacts on AP during natural depletion. Employing laboratory PVT data, the thermodynamics framework resulted in construction of an asphaltene precipitation envelope. This study implies that utilization of an appropriate PVT model along with the ICA-ANN approach in the investigation of AP leads to more reliable predictions compared to the conventional ANN and also a scaling model. The outcomes of this study appear to be useful in the design stage of more-efficient EOR processes. [ABSTRACT FROM AUTHOR]

Abstract: Australia has considerable hot rock geothermal energy potential. This results from the widespread occurrence of basement rocks (granites in particular) in which heat is generated by natural radioactive decay. There are extensive areas where temperatures are estimated to reach at least 200°C at around 5km depth. In Australia, there is also potential for lower temperature geothermal resources associated with naturally-circulating waters in aquifers deep in a number of sedimentary basins (Hot Sedimentary Aquifer Geothermal). These are potentially suitable for electricity generation and direct use. This paper will focus on the need to improve geothermal energy utilization in Australia, challenges facing it and the future benefits. This study shows, there is a significant potential for energy savings through greater use of geothermal energy in some regions of Australia. [Copyright &y& Elsevier]

ABSTRACT Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed-forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network-based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method-based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability. [ABSTRACT FROM AUTHOR]

Abstract: When a building is heated to a steady internal temperature, equilibrium is established in which the heat power into the building equals the rate at which heat is lost from the building. A good approximation of heat losses can be determined using the conventional generic equations which give a relatively accurate value of the heat loss; however these equations can be inaccurate, especially in applications where radiative heating is used. In this article simple predictive tools are developed to estimate the characteristic temperature ratios for both panel radiator and high temperature radiant strip systems. The calculated results can be used in follow up calculations for a model, which fully takes into account the radiation heat transfer occurring within a room space. The results of the proposed method are found to be in excellent agreement with reported data in the literature with average absolute deviation being around 0.2%. The predictive tool is simple and straightforward and it can be readily implemented in a standard spreadsheet program. The prime application of the method is as a quick-and-easy evaluation tool in conceptual development and scoping studies where a panel radiator system and a high temperature radiant strip system are being considered. The method may also serve as a benchmark in numerical and rigorous simulation studies. [Copyright &y& Elsevier]

Around 40% of the current world conventional oil productioncomes from carbonate reservoirs, dominantly mature and declining giantoilfields. Tertiary oil production methods as part of an EnhancedOil Recovery (EOR) scheme are inevitable after primary and secondaryoil production. The goal of surfactant flooding is to reduce the mobilityratio by lowering the interfacial tension between oil and water andmobilizing the residual oil. This paper highlights adsorption kineticsand equilibrium of Glycyrrhiza Glabra, a novel surfactant, in aqueoussolutions for EOR and reservoir stimulation purposes. A conductivitytechnique was used to assess adsorption of the surfactant in the aqueousphase. Batch experimental runs were also performed at various temperaturesto understand the effect of adsorbate dose on the sorption efficiency.The adsorption kinetics was experimentally investigated at room temperature(27 °C) by monitoring the uptake of the Glycyrrhiza Glabra asa function of time. The adsorption data were examined using differentadsorption equilibrium and kinetic models. The Langmuir isotherm suitsthe equilibrium data very well. A pseudo-second order kinetic modelcan satisfactorily estimate the kinetics of the surfactant adsorptionon carbonates. Results obtained from this research can help in selectingappropriate surfactants for design of EOR schemes and reservoir stimulationplans for carbonate reservoirs. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the physics of injection techniques required for large scale geological sequestration of CO2 is very important in order to mitigate the global warming in an effective and economical manner. In this paper, an analytical solution is presented to formulate the size of the CO2 droplets in terms of process parameters for Ex Situ Dissolution Approach (ESDA). The proposed solution is valid for turbulent bubbly flow regime and explicitly accounts for mass transfer and pressure changes along the pipeline length. Some technical and economic aspects of the ESDA are also addressed. In addition, the governing equation of mass transfer was numerically solved using the MATLAB® software. Comparison of the numerical and analytical results shows a good agreement for wide ranges of thermodynamic and flow conditions. The proposed analytical solution is very useful to estimate the length of pipeline required for full dissolution of CO2 droplets prior to injection into the ground. Results presented in this study indicate that the wellbore tubing could eliminate the need of horizontal mixing pipe in the ESDA. [Copyright &y& Elsevier]

[Display omitted] • MD simulation assesses effects of composition and operational conditions on asphaltene aggregation. • Effect of low concentration of OP on asphaltene aggregation is minor. • The most efficiency of OP is observed at 30 bar. • OP exhibits a greater performance at higher temperatures. • Sensitivity analysis assists to design an efficient inhibitor. The asphaltene stability in crude oil can be disturbed due to changes to thermodynamic conditions during production, raising flow assurance concerns. The addition of chemical inhibitors, such as surfactants, to a crude oil can improve the asphaltene stability. The impact of chemical inhibitors on the asphaltene aggregation intensity and aggregate characteristics, which is needed for design of an efficient inhibitor for a target crude oil, is poorly understood. In this paper, a molecular dynamics simulation tool is employed to simulate the molecular behaviour of the asphaltene and n -octylphenol (OP) at various concentrations of OP (0–15 wt%) and thermodynamic conditions (T = 300–360 K and P = 1–60 bar). To meet the objectives, the average aggregation number of different asphaltenes and asphaltene mixtures is measured besides the aggregate characteristics, including aggregate gyration radius, density, and shape. The results show that the archipelago asphaltenes (A1) do not self-aggregate severely, and the gyration radius distribution of the aggregates is similar after the OP addition. Nevertheless, the average aggregation number and the aggregate density analysis show that the aggregation intensity reduces slightly when the OP concentration is increased. Because the continental asphaltene (A2) can form hydrogen bonds, the addition of OP increases the aggregate dispersity significantly, which is identified with the highest peaks at 28 and 35 Å after the addition of 7 and 15 wt% OP, respectively. It is found that OP increases the variations in the aggregate shape and type for A2 aggregates. It is concluded that a high concentration of OP (above 7 wt%) is needed to avoid the asphaltene aggregation. The pressure increase does not change the aggregate shape both with or without the OP. However, adding OP increases the aggregate gyration radius and decreases the aggregate density at 30 bar. Increasing temperature results in a severe aggregation in the system without the inhibitor as the aggregate gyration radius increases, and aggregates become more compact and spherical. However, by adding OP, both the average aggregation number and aggregate gyration radius decrease (especially at 360 K). According to the sensitivity analysis results, integrating GPUs with CPUs can speed up the simulation approximately three times, compared to the system processing with only CPUs. Outcomes from this research work can help for a better understanding of the molecular behaviour of asphaltene and inhibitors at various thermodynamic situations, leading to the improvement of inhibitor design. This paper highlights the competence of molecular dynamics for exploring the optimal inhibitor concentration and the thermodynamic conditions in which the inhibitor has the highest impact on asphaltene aggregation. [ABSTRACT FROM AUTHOR]

Recently, mesoporous silica nanoparticles (MSNs) have emerged as promising candidates in the field of hemorrhage control owing to their extended pore size, high surface area, and excellent biocompatibility. These characteristics directly influence the toxicity of cells, the loading of therapeutic agents, and the release of active ions during the hemostasis process. Therefore, understanding the fundamentals of tuning these characteristics is important to design these types of carriers. While several literature reviews have explored the role of MSNs in hemorrhage control, comprehensive studies focusing on their general characteristics and specific applications remain scarce. This review concentrates on the principles of synthesizing mesoporous silica, the general types of MSNs, techniques for loading drugs methods onto the site of injury, release kinetics models, biocompatibility, toxicity, and the unique properties of MSNs. Furthermore, the article examines the mechanism of action of MSNs as nanomaterial hemostatic agents. [ABSTRACT FROM AUTHOR]

Conventional methods of energy storage are not able to provide long-term storage due to practical and economic constraints. One of the leading methods for long-term energy storage is the use of wind energy to liquefy hydrogen and oxygen. In this study, an integrated structure of hydrogen liquefaction is developed using the wind turbines, Kalina power generation cycle, and electrolyzer. The HYSYS and TRNSYS software packages with MATLAB programming are used to simulate the hydrogen and oxygen liquefaction structure, considering the weather conditions of the province of Newfoundland and Labrador (NL), Canada. This integrated structure produces 2100 kgmol/h of liquid hydrogen by receiving 264.1 MW of power from wind turbines. The waste heat of the hydrogen liquefaction cycle is used to supply the Kalina power generation cycle. Thermal (or energy) integration can reduce the power consumption of the integrated structure by 8.61%. The specific energy consumption, coefficient of performance of the hydrogen liquefaction cycle, and energy efficiency of the Kalina cycle are obtained to be 5.462 kWh/kgH 2 , 0.1384, and 14.06%, respectively. The overall exergy efficiency and total irreversibilities are 58.73% and 112.7 MW, respectively. The exergy analysis of the integrated structure shows that the highest exergy destruction occurs in electrolyzers (83.13%) and heat exchangers (5.93%), respectively. Also, by adding oxygen liquefaction flow to the integrated hydrogen liquefaction cycle, the specific energy consumption and total exergy efficiency are determined to be 1.632 kWh/kg liquids and 59.11%, respectively. The sensitivity analysis to investigate the effects of the important variables on the performance of the integrated structure is also performed. [Display omitted] • Hydrogen liquefaction cycle, Kalina power cycle, and electrolyzer are efficiently integrated. • Wind turbines are used based on weather conditions of Newfoundland and Labrador. • SEC and COP of hydrogen liquefaction cycle are 5.462 kWh/kgH 2 and 0.1384, respectively. • Kalina cycle and exergy efficiencies of the hybrid system are 14.06% and 58.73%. • Integrated process can produce 2100 kgmol/h liquid hydrogen and 1077 kgmol/h oxygen. [ABSTRACT FROM AUTHOR]