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In this study, an optimal scheme is proposed by utilizing waste streams at a plant-wide scale along with pressure retarded osmosis (PRO) membrane allocation in complex industrial networks for energy recovery. Chemical exergy pinch analysis (ChExPA) and process graph (P-graph) are used to address the problem. The ultimate goals of utilizing the tools are (1) determine the optimal external load consumption, (2) minimizing the waste discharge, and (3) sustainable energy production while utilizing high chemical exergy potential waste discharges. A reliability assessment assisted with Monte-Carlo simulation is further performed to evaluate the proposed solutions from P-graph considering uncertainties. The effectiveness of the proposed methodology is explained using three industrial case studies which covered both intra-plant and inter-plant networks. The results indicated that ChExPA and P-graph can effectively identify the optimal location of the PRO membrane in industrial networks. Upon analyzing the complex inter-plant industrial networks 7.795 MW net power output was harnessed, and significantly higher waste of 384.92 kg/s was recovered with a levelized cost of energy of 0.073 $/kWh. The inter-plant network shows the greatest net profit which accounted for approximately $1,191,000 (i.e., 5.84 times higher than stand-alone plants) with a reasonable payback-period of 4.5 years. [ABSTRACT FROM AUTHOR]

Tungsten oxide (WO 3) is a key photoanode for the photoelectrochemical water splitting (PEC) application; however, it comes with the challenges of lower efficiency and larger photogenerated charge recombination. Oxygen vacancies are always favorable, up to some extent, to hinder this critical issue. In this study, we synthesized cerium (Ce) doped monoclinic WO 3 with tuning of its work function along with the suitable band edge position for the PEC water splitting applications. Moreover, it was observed that the crystal phase was changed by a certain doping of Ce to {002} plane with 1D morphology, which in turn alters conduction band position as well as the Fermi energy level. The WO 3 with 0.49 at % of cerium doping showed a record photocurrent up to ∼2.48 mA cm−2 1.23V vs. RHE as compared to the undoped WO 3 , which exhibited 1.53 mA cm−2 at 1.23V vs. RHE. It was observed that the WO 3 with 0.49 at % of cerium doping showed charge separation efficiency up to ∼79% as compared to the undoped WO 3 sample, which showed only 20%, which is one of the reasons for this elevated performance of the doped sample. Overall, the results indicate that Ce doping is beneficial for WO 3 PEC applications and further it was confirmed by the DFT calculations as well. • An optimized 0.49 at % of Ce doping showed a record photocurrent of ∼2.48 mA cm−2 at 1.23V vs. RHE. • Facet Reorientation and mixed valence states. • Enhancements in oxygen vacancies and shift of conduction band upon Ce doping was observed. • DFT simulations confirmed the alteration in density of states and band gap. [ABSTRACT FROM AUTHOR]

The structural and chemical architecture of the active electrode material must provide an optimum environment for charge storage as well as stability to provide crucial supercapacitance properties. In this study, we developed a binder-free protocol to obtain an optimized polymorph of lead vanadate (PbV) with a unique hybrid architecture on nickel foam for hybrid supercapacitor applications. Furthermore, a detailed capacitance power prediction model is proposed based on several machine learning and recurrent neural network methods to improve the reliability of the fabricated hybrid supercapacitor. Specifically, recursive feature elimination is implemented to alleviate the problems of overfitting and the curse of dimensionality by identifying and eliminating extraneous characteristics while determining the appropriate features of the capacitance power. The experimental observations are validated by DFT analysis concerning the role of oxygen vacancies for the improvement of electrical characteristics. Ultimately, the fabricated energy storage device exhibits impressive energy and power densities of 26.18 Whkg−1 and 6 −1, respectively. Importantly, the predicted results confirm that the long-short-term memory network provides excellent results for capacitance prediction of hybrid supercapacitors with R2 = 0.83 in the test set and synthesized sample prediction with a mean absolute error of 20.37. • A facile, single step, cheap and binder free fabrication of electroactive electrode. • DFT analysis was utilized to understand the excellent charge storage performances. • RNN was applied to validate the experimental results of hybrid supercapacitor device. • Design guidelines are offered to tune key parameters for supercapacitors. [ABSTRACT FROM AUTHOR]

In the present study, a power, cooling, and freshwater tri-generation system is proposed to meet the global requirements sustainably. A Kalina cycle (KC), an absorption refrigeration cycle (AC), and an organic Rankine cycle (ORC) coupled with reverse osmosis (RO) are integrated to form the proposed system. The performance of the proposed system is analyzed using thermodynamic and economic viewpoint. An integrated system combines the refrigerant loop of the water–ammonia absorption chiller, consisting of an evaporator and throttling valves with KC. The Kalina turbine discharges the heat and combines with generator to loop the ORC and generate power which drives the RO module. A portion of the mass flowrate enters the evaporator to generate cooling after being condensed in the AC unit. The effect of key thermodynamic parameters on system performance is studied using parametric analysis. The results show that the system is capable to generate 1,725 kW of power, 665 kW of cooling, and 3.42 m³/h of freshwater. The parametric analysis results indicate that the flash tank pressure has an optimum value which should be selected wisely. It is concluded that the parameters related to the KC are dominant ones because they can affect both the KC and the ORC. The proposed system is a flexible adapting power, cooling, and freshwater tri-generation demand. [ABSTRACT FROM AUTHOR]

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to in vitro methods to predict the toxicological effects of chemicals on human health. In this study, we assessed a deep-learning based QSAR (DL-QSAR) model to predict the qualitative and the quantitative effects of EDCs on the human endocrine system, and especially sex-hormone binding globulin (SHBG) and estrogen receptor (ER). Statistical analyses of the qualitative responses indicated that the accuracies of all three DL-QSAR methods were above 90%, and greater than the other statistical and machine learning models, indicating excellent classification performance. The quantitative analyses, as assessed using deep-neural-network-based QSAR (DNN-QSAR), resulted in a coefficient of determination (R2) of 0.80 and predictive square correlation coefficient (Q2) of 0.86, which implied satisfactory goodness of fit and predictive ability. Thus, DNN was able to transform sparse molecular descriptors into higher dimensional spaces, and was superior for assessment qualitative responses. Moreover, DNN-QSAR demonstrated excellent performance in the discipline of computational chemistry by handling multicollinearity and overfitting problems. Image 1 • VIP and LASSO regression were implemented to select key molecular descriptors. • DL-QSAR model was used to predict the responses of EDCs to SHBG and ER. • DNN-QSAR model obtained Q2 of 0.86 prediction performance. • DNN-QSAR model showed accuracy of 97.0% for classification performance. • Model performance of DL-QSAR models outperformed other conventional ML techniques. DL-QSAR models were implemented to predict and classify the responses of the ER and SHBG to EDCs. The performances of proposed models were superior to conventional ML techniques. [ABSTRACT FROM AUTHOR]

A novel and efficient multigeneration system aiming to achieve near-zero CO 2 emissions is proposed. The system employs a methanol production process utilizing captured CO 2 from the flue gas of a biomass-gasification plant. The proposed system successfully generates green H 2 through water electrolysis, supported by thermal power, with a portion of the hydrogen reacting with CO 2 to produce e-methanol. Additionally, the system produces other valuable products, including power, cooling, O 2 , and CO 2 , in a coherent manner, benefiting from a low-emission framework and exhibiting high thermodynamic performance. The proposed multigeneration system consists of a biomass-gasification-based power plant, a water electrolyzer, a methanol generation unit, a carbon capture unit, and a steam jet ejector-based refrigeration cycle. The integrated multigeneration system is analyzed from the energy, exergy, and economic aspects. The results demonstrate the system's ability to achieve production rates of 430.25 kg/h of e-methanol and 190 kg/h of green H 2. Furthermore, the energy and exergy efficiencies of the system are found to be 78.13% and 71.63%, respectively. The CO 2 emissions analysis reveals that the proposed system significantly reduced total CO 2 emission to 78.5% (0.61 kg CO2 /kg). The total cost of production is estimated to be 0.087 $/kg. [Display omitted] • Novel integrated biomass-based multigeneration system for power, methanol, H 2 , O 2 , and Cooling. • Captured CO 2 is utilized to produce synthetic e-methanol. • Flue gas from gasification mixed with H 2 from electrolyzer for methanol synthesis. • Obtained the total energy and exergy efficiencies of 78.1 % and 71.6 %, respectively. • Total methanol production and total production cost to be 430.25 kg/h and 0.108$/kg. [ABSTRACT FROM AUTHOR]

In the present study, an optimal power and freshwater cogeneration system is proposed to meet the global requirements sustainably. A Rankine cycle (RC), an organic Rankine cycle (ORC) and a reverse osmosis (RO) module are integrated to form the proposed system. The performance of the system is investigated using thermo-mathematical models allocating seven organic fluids in the bottoming ORC. A novel evolutionary algorithm-based multi-objective optimization approach is applied using thermorisk and thermoeconomic analyses. Thus, an optimal configuration is determined at both global and local scales. Finally, a flexibility analysis is performed to the optimal configuration considering probable uncertainties in the market. The optimization results showed that the total accidental risk impact and the total product cost rate improved by 2.49–48.73% and 5.67–62.41%, respectively, depending on the employed organic fluid. The highest exergetic efficiency and the minimum specific power consumption were obtained as 52.74% and 4.111 kWh/m 3 , allocating R245fa in the optimal system. The system enjoying R123 had the widest flexibility range without any increases in the optimum total product costs. [ABSTRACT FROM AUTHOR]

The hybrid desalination of reverse osmosis (RO) with forward osmosis (FO) is an innovative technology for freshwater production worldwide, which provides many advantages such as RO fouling and scaling, recovery of the energy of RO brine, minimizing the power consumption and minimizing the use of chemicals required for conventional pretreatment steps. For this reason, breakdowns, blockage of the membrane, pressure losses and preventive maintenance have to be minimized in duration and frequency to ensure the maximum availability and indeed improving the availability or reliability of the system leads objectively to the reduction of operating costs. In this study, hybrid fault tree analysis method based fuzzy set theory was considered to evaluate the availability and reliability of the integrated RO–FO desalination system. For assessing availability, the system was divided into four subsystems such as seawater intake pump, pressurization stage, FO stage and RO stage. The data were collected from reliability data bank and probabilities estimated by considering the fuzzy set theory, wherever detailed data were not available. The overall unavailability of the hybrid system was 0.0156. [ABSTRACT FROM AUTHOR]

Over the past decades, forecasting electricity demand has been crucial in energy planning and power distribution management to establish sustainable strategies in undeveloped countries. This work proposes an integrated approach to evaluate Pakistan's energy sector and holistically investigate electricity-load strategies to prioritize them and prevent future electricity crises. This paper proposes a renewable energy technologies-based system design to eliminate energy shortfalls. Deep learning models can predict fluctuating variation in energy consumption, crucial for designing renewable energy systems. The strengths – weaknesses – opportunities, and threats (SWOT) analysis identified relevant factors and sub-factors, while the fuzzy-TOPSIS methodology prioritized alternative strategies. The results show that driving the nation to the functionality of an energy hub by sufficiently using its geostrategic location within the regional cooperation framework is the critical dominant factor. Besides, improving the country's investment environment was the least favored priority. Baluchistan was found to have the highest potential for wind energy, with an ability to generate 3270 MWh/year through wind power. [Display omitted] • An integrated management approach is proposed to address electricity crisis. • The proposed strategical energy forecast and planning is studied for Pakistan. • Prioritize the indicated strategies for sustainable energy planning and development. • Turning the country into renewable energy hub is suitable for Pakistan's economy. • Baluchistan has the potential to generate 3270 MW power from wind power. [ABSTRACT FROM AUTHOR]

A main challenge in rapid nitrogen removal from rejected water in wastewater treatment plants (WWTPs) is growth of biomass by nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB). In this study, partial nitritation (PN) coupled with air-lift granular unit (AGU) technology was applied to enhance nitrogen-removal efficiency in WWTPs. For successful PN process at high-nitrogen-influent conditions, a pH of 7.5–8 for high free-ammonia concentrations and AOB for growth of total bacterial populations are required. The PN process in a sequential batch reactor (SBR) with AGU was modeled as an activated sludge model (ASM), and dynamic calibration using full-scale plant data was performed to enhance aeration in the reactor and improve the nitrite-to-ammonia ratio in the PN effluent. In steady-state and dynamic calibrations, the measured and modeled values of the output were in close agreement. Sensitivity analysis revealed that the kinetic and stoichiometric parameters are associated with growth and decay of heterotrophs, AOB, and NOB microorganisms. Overall, 80% of the calibrated data fit the measured data. Stage 1 of the dynamic calibration showed NO 2 and NO 3 values close to 240 mg/L and 100 mg/L, respectively. Stage 2 showed NH 4 values of 200 mg/L at day 30 with the calibrated effluent NO 2 and NO 3 value of 250 mg/L. In stage 3, effluent NH 4 concentration was 200 mg/L at day 60. [Display omitted] • Dynamic calibration for operation with PN air-lift granular technology. • Steady state/dynamic calibrations for sensitivity analysis influential parameters. • 20 stoichiometric and kinetic parameters chosen for steady/dynamic calibration. • Temperature 20 °C–30 °C were evaluated for performance enhancement. [ABSTRACT FROM AUTHOR]

• Optimum PRO network presented for recovery of chemical exergy resources. • Benders' decomposition approach used to classify new tri-level framework. • Circular integration notion used to depict industrial self-sustainability. • Tri-level decomposition methodology reduced total cost of network by 19.29 %. • Proposed method tackles network uncertainty while retaining chemical exergy issues. Climate change, resource scarcity, waste reduction, and the lack of sustainability in process industries are vital concerns that civilization confronts. The process integration methodology may aid such sustainability. In this study, a novel chemical exergy resource recovery network is proposed for optimal energy and waste recovery in high-salinity-gradient chemical industries using a pressure-retarded osmosis membrane while indicating a self-sustainability and allocation in complex industrial networks. The mathematical programming paradigm is expressed as a multilevel optimization model to introduce novel ideas for explicitly modeling the trade-offs between waste and energy flows in circular integration while demonstrating industrial network's environmental impact assessment. This problem is decomposed into two subproblems (SPs) that must be addressed sequentially. The first SP is designed to decrease the total cost of the network while reducing external resource use. The second SP formulates a mixed-integer nonlinear programming model with the objective of minimizing the environmental effects and exergy consumption rate of the network. A case study of a naphthalene-methaforming plant demonstrates the efficacy of the proposed methodology. The results showed that using a tri-level optimization technique, a considerable improvement in flowrate, total annualized cost, and energy recovery is obtained while limiting the network's environmental impact. The operating phase accounts for approximately 75% of the global warming potential output. The proposed tri-level approach based on Benders' decomposition approach reduced the overall cost of the network by 19.29%, and 47.42 MW net power is recovered in the case study. In addition, the circular exergy use rate and environmental factor were reduced from 400 to 0.037 MW and from 2.569 to 1.481 kgCO 2 /year, respectively, using the tri-level decomposition approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • An optimal DSM-based scheduling of energy distribution network is developed. • Strategical conversation of regulated mix-generation reduced 0.92–2.28% of deficit. • Self-production of installing PVs aided to increase beneficial cost consumption. • The energy distribution network S.IV satisfied top 5 appliance of consumer request. • Combined low-carbon energy in DSM-based distribution reduced 27.04 tons CO 2. Global transactive energy has become more complex due to the extension of its efficiency. Thus, multi-residential demand responses pose a new challenge in the critical infrastructure of power transition networks in energy planning and management. By encompassing optimal scheduling with strategic energy decentralization, a reliable distribution network can suggest a resolution that contributes to optimize energy efficiency. This research proposed a holistic bidirectional distribution network with optimal scheduling-based demand-side management (DSM) for superior power sharing and beneficial performance. A DSM was developed to maintain equal power sharing between various mix-energy sources, providing a sub-level residential demand response program. Among several scenarios considered, the DSM-based scheduling distribution network resulted in optimum and equivalent sharing of power loads for residential demands. Moreover, the application of self-production-based renewable energy source (RES) is a novel strategy to improve imbalanced power sharing. The energy deficit and surplus can be solved by establishing a conservation strategy with self-installation-based photovoltaic panels by end-users. CO 2 emissions were also estimated to elucidate the effect of the energy distribution network on the environment according to different category of energy sources. Considering satisfaction analysis based on appliance energy consumption, the energy distribution network was efficient in improving the end-user expenditure, sustaining a clean energy loading profile, and maximizing the benefit of cost. [ABSTRACT FROM AUTHOR]

A holistic analytical and smart management approach is proposed to investigate the performance of renewable-based tri-generation system to generate power, cooling, and domestic hot water with a simultaneous consideration of several operational, design, and system feasibility aspects in a framework. The proposed configuration consisted of a solar driven organic Rankine cycle, and double-effect absorption refrigeration cycle integrated with a Kalina cycle. To analyze environmental implications, life cycle assessments are performed, while further evaluations are conducted utilizing algebraic thermo-mathematical programming. Hazard study and thermal reliability analysis are also performed to analyze the safety and failure rate of the system. Additionally, four critical scenarios are defined: safe urban deployment, economic viability, reliable operation, and sustainable development. In accordance with the four specified scenarios the integrated system is globally optimized using a weighted multi-objective optimization. Following that, a suitable optimum system and fluid allocation is conducted based on hybrid deterministic decision-making technique under smart management. The optimization results showed that the total exergorisk, system reliability, and environmental impact can be simultaneously improved in the range of 4.08–28.3%, 4.14–13.9%, and 1.65–24.6%, in all scenarios employing various working fluids, respectively. Additionally, the system achieves lowest overall cost rate (4.17USD·s−1) with R113, while the highest energetic efficiency (46.3%) and system reliability of (91.2%) was associated with R365mfc. Finally, a comparative analysis indicates a CO 2 saving potential of 6646, 4883, and 2878 tons/year in comparison to coal, fuel oil, and natural gas based integrated energy systems. [Display omitted] • Solar integrated energy system for power, cooling, and domestic heating load. • Comparative economic, risk, reliability, and life cycle assessment were performed. • Real world policies were adopted for exergy models in four scenarios. • Weighted optimization and smart decision-making are utilized for fluid allocation. • System presents CO 2 emissions and CO 2 cost savings of 6646 ton/Yr and 87,360 USD/Yr. [ABSTRACT FROM AUTHOR]

Safety-risk management in the hydrocarbon processing industry necessitates the availability of appropriate data and decision-making tools. Operational factors may influence chemical and physical risk occurrences associated with high-hazard plant operation in the chemical process sectors. Thus, safety-risk technologies and assessments should be explored considering potential events that result in fatalities, property destruction, economic loss, and environmental degradation. This research established a systematic framework for reducing safety risks to decrease accidents and hazards associated with the inherent production processes, as well as proposing process-level maintenance techniques for the specified hydrocarbon processing industry. First, the hazards of chemical reactivity were studied to determine which equipment performance poses the greatest risk. Physical and chemical risks were obtained to configure out identical nodes of expressing severe hazard via a qualitative assessment using the HAZOP study. The fuzzy best-to-worst technique combined with an analytical network process (fuzzy-BWANP) was utilized to assess the safety-risk criticality due to the operational reaction process, environmental risk, economical safety, and occupational management. Furthermore, the annual loss of expectancy and exposure factors for the earlier categories based on their operational expenses and failure time were estimated. The case study examined how to increase the process feasibility of an acrylonitrile plant. Consideration of economic loss resulted in the extraction of a risk index that helps decision-makers in determining priority tasks for addressing urgent hazards in any hydrocarbon processing industry's safety management. [Display omitted] • An integrated qualitative-quantitative safety-risk approach was developed. • Twenty-nine risks factors in an acrylonitrile plant (AN) case were identified. • Safety-risk analysis was investigated via chemical reactivity and physical damage. • Pressure variation in scrubber was identified as the most crucial safety problem. • Process disruption, S14, was got highest risk causality from fuzzy-BWANP approach. [ABSTRACT FROM AUTHOR]

This study developed a data-based soft-sensor to predict indoor PM 2.5 from easy-to-measure outdoor and indoor air variables. The method consists of neural circuit policies (NCP), nature-inspired liquid time-constant networks (LTC), a subclass of continuous recurrent neural networks (RNN) represented by an ordinary differential equation (ODE) system to be adapted to each instance. The performance metrics indicated that the NCP yielded the most accurate predictive performance accounting for an improvement compared to other neural methods accounting for 27%–30%. On the other hand, a health risk warning assessment was used to evaluate the NCP capability to detect whether the indoor PM 2.5 concentration falls within an 'unhealthy for sensitive groups' health risk level. Finally, the NCP soft-sensor model is evaluated into the ventilation control system of the D-subway station, making the comprehensive indoor air quality index (CIAI) stay in a moderate range without any violation of unhealthy breakpoints in contrast to the rule-based ventilation system. [Display omitted] • Neural circuit policies are proposed to forecast PM 2.5 sequences in a subway station. • The NCP showed superiority in modeling PM 2.5 compared to different methods. • A near pollution threshold analysis evaluated health risk levels detection. • The NCP showed acceptable capability for supporting warning systems. • The NCP supported a proper subway ventilation management for PM 2.5 reduction. [ABSTRACT FROM AUTHOR]

Nowadays, the transportation sector occupies a vital role in current society. However, this sector is the second-highest greenhouse gas emitter worldwide due to fossil fuel combustion. Transitioning from conventional to renewable energy propulsion is a promising alternative to climate change mitigation. This study proposes a smart decision-making approach of an integrated system assisted with renewable energy sources to satisfy the dynamic railway electrification demand. The proposed energy system consists of a proton-exchange membrane electrolyzer, solid-oxide fuel cell, and lithium-bromide absorption chiller assisted with solar radiation and wind turbine to simultaneously generate power, hydrogen, cooling, and heating loads. A novel approach consists of power-pinch analysis and multi-criteria decision-making (MCDM) to determine the optimal sizing of renewable resources considering the system's thermodynamic, economic, and exergy performance. A total of five optimal scenarios with different renewable sources share were obtained. Accordingly, the scenario with a share of 70/30 of solar and wind energy showed the highest competitiveness based on the MCDM. This scenario showed an energetic and exergetic efficiency of 49% and 34%, respectively. Furthermore, it yielded acceptable subproducts generation, including the production of 312.25 kg H 2 /day, in an economical budget with a levelized cost of energy (LCOE) value of 0.079 $/kWh. • Smart management tool was developed for the dynamic railway electrification demand. • Power, hydrogen, cooling, and heating loads were produced simultaneously. • Power pinch analysis was developed to find optimal sizing of renewable energy share. • Energy and exergy analyses yielded efficiencies of 69% and 42%, respectively. • The optimal scenario generated a total of 312.25 kg H 2 /day. [ABSTRACT FROM AUTHOR]

This study presents a novel multi-generation system (MGS) assisted by sugarcane bagasse to produce power, freshwater, and cooling. The proposed integrated MGS consisted of a bagasse-biomass based gasifier-Brayton cycle, a Rankine cycle, a Kalina cycle, an ejector refrigeration cycle, and a multi-effect desalination unit. Comprehensive energy, economic, exergy, exergorisk (4E) analyses of the proposed system were performed. The effects of operating parameters on thermodynamic performance and economic feasibility were investigated. An optimal configuration of the proposed system was determined via weighted multi-objective optimization approach considering exergorisk, exergy, and economic analyses. The results showed that bagasse-biomass flowrate was the dominant factor affecting variation in energy and exergy efficiencies, and total cost rate. An increase in bagasse-biomass flowrate from 1.5 kg/s to 10 kg/s led to decreases of 34.42% and 50.75% in overall energy and exergy efficiencies. The most substantial increase (43.07%) in exergy efficiency occurred at a high compression ratio. The optimization results showed that the total accidental risk impact was improved by 92.59% and energetic and exergetic efficiency was increased to 92.10% and 77.49%, respectively. The proposed optimum system can provide power, cooling, and freshwater at loads of 28.72 MW, 13.64 kg/s, and 3.43 MW, respectively. Image 1 • Bagasse gasification based novel sustainable multi-generation system is proposed. • Investigating sensitivity of system performance and costs to the key parameters. • The system is optimized decreasing total cost rate and exergorsik analysis. • Optimized system has high energetic and exergetic efficiency (92.1% and 77.49%). • Bagasse biomass contribution leads to higher power generation. [ABSTRACT FROM AUTHOR]

• Long term forecasting of daily weather characteristics and monthly energy demand. • Nationwide deployment of hybrid renewable energy system based on solar and wind. • Power-to-X extension to produce sustainable hydrogen and synthetic natural gas. • Reliability assessment and mitigation plan with the aid of Monte-Carlo simulation. • Deployment priority of regions according to assessment criteria via TOPSIS. The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H 2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS. [ABSTRACT FROM AUTHOR]

• A novel steam jet ejector power plant was proposed to adapt to climate change. • A new genetic algorithm solved the multi-objective optimization model. • Two wet cold utilities were evaluated via four new water-exergy nexus criteria. • The novel system loses 63.65% less water than available steam power plants. • The thermal efficiency of the proposed eco-friendly system reached 40.59%. Steam power plants contribute to anthropogenic climate change by emitting a considerable amount of carbon dioxide and losing significant freshwater. In the present study, a novel model-based steam jet ejector power plant is optimally designed to adapt to climate change by minimizing water losses and greenhouse emissions. An iterative thermo-mathematical program and a nonlinear mathematical model represent the system's energy and economic models. At the same time, an exergy analysis evaluates its performance according to the Second law of thermodynamics. A non-dominated sorting genetic algorithm generation III is interfaced with the developed empirical program to solve a multi-objective optimization model. Three decision-making scenarios including a maximum thermal efficiency (I), minimum cost of energy (II), and minimum heat losses (III) are considered to select appropriate configurations in the final Pareto front. Subsequently, a natural draft wet cooling tower and a once-through cooling system are coupled with the optimal configurations. The performance of the fully-integrated systems is evaluated using four new water-exergy nexus criteria including water withdrawal for exergy dissipation, lost exergy for exergy dissipation, water consumption for fuel and product exergies. Finally, a sensitivity analysis is performed to take the environmental fluctuations and model uncertainties into account. The comparative results showed that water consumption for product and fuel exergies improved by 63.65 and 64.08%, while water withdrawal for exergy dissipation of the system was approximately nine times smaller than available model-based steam power plants. Moreover, the energy efficiency of the proposed system was 1.42% greater than the previous systems at the cost of 0.018 $/MJ more cost of energy. Thus, the proposed system can tackle drought and mitigate climate change with more investment in the energy sector. [ABSTRACT FROM AUTHOR]

• A chemical exergy pinch analysis is proposed via optimal pressure retarded osmosis. • The graphical tool is detailed using grand composite curves. • The numerical tool is detailed using cascade tables under three scenarios. • The high exergetic potential of 1384.7 MW is diagnosed in the sugar mill. • The PRO module could commercially recover 11.3 MW waste energy with 0.038 $/kWh. In the present study, pinch analysis is extended taking chemical exergy concept into account. The novel chemical exergy pinch analysis is proposed for sustainable power production by an economic application of pressure retarded osmosis membranes in chemical industries. Chemical exergy composite curves and chemical exergy cascade tables are developed as graphical and numerical tools, respectively. The tools are used to obtain maximum waste energy recovery by achieving various targets and determining the pinch point in a salinity gradient network. Thus, maximum energy recovery and minimum waste treatment are targeted, simultaneously. Moreover, a mathematical model follows the chemical exergy pinch analysis for an economic evaluation of pressure retarded osmosis-retrofitted industries under three probable scenarios. A sugar mill plant is simulated as the case study to validate the model-based analysis. The results showed that chemical exergy pinch analysis could efficiently provide the optimal pressure retarded osmosis -retrofitted industrial networks for decision-making. Having analysed the complex chemical exergy streams by chemical exergy pinch analysis, 11.30 MW net power is recovered with 0.038 $/kWh levelized cost of energy in the case study. [ABSTRACT FROM AUTHOR]

Renewable energy resources have become important to strategies for meeting industrial demands for power due to their various benefits when coupled with government incentives and public support. To obtain a reasonable compromise between capital investment costs and system reliability, a method of determining the optimal size of hybrid renewable energy system (HRES) components is needed. This study proposes a smart management approach to optimal sizing and power management of hybrid photovoltaic-wind turbine generation with hydrogen and battery storage, considering contemporary economics, system reliability, and environmental policies to meet the dynamic energy demands of a wastewater treatment plant (WWTP). A multi-objective approach based on a fuzzy–decision-making method is proposed to solve the optimization problem. Extended power-pinch analysis was applied to optimize the size of components and determine feasible electrical-storage capacity to tackle the dynamic power loads of the WWTP. A year of observations showed that an HRES has a high reliability level for energy loading up to 86% when supplying a dynamic demand, with acceptable environmental emissions and an economical budget. • An optimal hybrid renewable energy system configuration is proposed for a WWTP. • The power strategy is managed by reliability, economic and environmental factors. • The multi-objective criteria decision is targeted at dynamic demand and supply. • An optimal design comprises 165 photovoltaic panels and 5 wind turbines. • Reliability performance is 80%, with acceptable environmental and economic results. [ABSTRACT FROM AUTHOR]

• A comprehensive analysis for evaluating energy conversion systems is described. • A novel water-exergy nexus analysis is employed to evaluate hot and cold utilities. • A mathematical program is developed to model two integrated energy systems. • According to 4ER, power generation was preferred to power/cooling cogeneration. • Allocating steam caused less risks and costs but great water losses in cold utility. A holistic analytical approach is proposed to study the performance of fossil fuel burning integrated energy conversion systems considering energetic, economic, exergetic, environmental and risk (4ER) aspects in a framework. For this, life cycle assessment is conducted to study environmental impacts while other analyses are performed using the algebraic thermo-mathematical programming. The hazardous risks are also investigated using a hazard and operability approach. The external hot and cold utilities are also studied using a novel water-exergy nexus (WExN) analysis. Accordingly, two configurations are developed that integrate a Rankine cycle (RC) and an ejector refrigeration cycle (ERC) for two purposes: power and cooling co-generation (CGS) and power generation (MGS). Water losses in both systems are studied considering three cold utilities and two fossil fuel cycles using the WExN analysis, and the performance of the CGS and the MGS are compared employing several organic fluids. The results showed that the MGS had greater energetic and exergetic efficiencies, better environmental performance, and less hazardous risk impacts compared to the CGS employing almost all working fluids. The smallest exergy loss in the cooling system was 3.90 MW and 7.94 MW in the MGS allocating R123 and the CGS using R718, respectively. [ABSTRACT FROM AUTHOR]

The present study investigates the performance of a hybrid solar thermal membrane-based multigeneration system for generating power, cooling, heating, and water. The integrated arrangement consists of organic Rankine cycle (ORC), Kalina cycle (KC), ammonia-water refrigeration, Pressure retarded osmosis (PRO) and forward osmosis (FO). A comprehensive multicriteria assessment of energy, exergy, economic, environmental, and emergy (5 E) is conducted to ensure a systematic examination. The results indicate that the allocation of R113 in the ORC yielded the highest exergetic efficiency and energy cost of 46.61% and 0.08109 $/kWh, respectively. Additionally, the system achieved the lowest environmental impact of 1694 mPts.h−1 when employing R718. The utilization of rejected water–ammonia in the PRO and FO modules achieves a reasonable power density and water flux of 17.5 W m−2 and 67.94 l m−2h−1, respectively. Finally, emergy-based sustainability analysis revealed that the proposed system obtains a sustainability index of 0.73, which is greater than that of the fossil fuel-based power generation systems. Comparison of three considered scenarios based on emergy and economic analysis demonstrated that fossil fuel driven system is not providing sustainable products even with a 72.3% decrease in initial capital cost. For further improvements, we highlight several areas for additional research and provide recommendations for existing solar- and membrane-driven systems. [Display omitted] • A novel integrated solar-membrane driven multigeneration system in designed. • A comprehensive energy, exergy economic, environment and emergy analysis were performed. • Pressure retarded osmosis can produces power density of 17.5 W m−2 under operational condition. • The highest energy and exergy efficiencies are 49.53% and 46.61%, respectively. • The emergy based sustainability analysis of the system is equal to 0.73. [ABSTRACT FROM AUTHOR]

A modified sulfur-iodine cycle with a non-nuclear heat source is proposed to enhance the economics and reduce risk and damage to society in terms of cost. A modified sulfur cycle employs a steam boiler as a heat source. The modified sulfur iodine cycle is composed of fewer reactions than the original. Thermodynamic feasibility analysis, economic evaluation, risk assessment, and socio-economic analysis are carried out on both the sulfur-iodine cycle and modified sulfur-iodine cycle, and the results are compared. 50.9 kJ/mol of steam was required for the minimum entropy range without the violation of the second law. The results show that the modified process is thermodynamically feasible with a positive entropy region at operating temperatures. The capital cost and operating cost are reduced by 40% and 29% for 1 kmol/h hydrogen production, respectively. The failure rate in the modified process is reduced by 64% compared to that of the original. The social health cost in the modified cycle is reduced by 41% compared to that of the original. Image 1 [ABSTRACT FROM AUTHOR]