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Carbon–hydrogen–oxygen symbiotic networks (CHOSYNs) provide a systematic multiscale framework for integrating multiple hydrocarbon processing plants. Earlier approaches have focused on high-level p...

Power to gas (PtG) capitalizes on the free or low-cost power made available during frequent mismatches between power demand and power supply by converting water into hydrogen and oxygen via electrolysis. Current PtG projects vent the oxygen to the atmosphere and utilize the hydrogen for myriad purposes. Although oxygen has relatively little value when compared to hydrogen on a mass basis, the value of oxygen produced via electrolysis is significant compared to the value of the hydrogen due to the composition of water. This work presents a systematic methodology for designing a PtG project which co-utilizes hydrogen and oxygen to manufacture hydrogen and chemicals via integration with the natural gas infrastructure. A case study is developed and solved to demonstrate the applicability of the proposed methodology. The results of the case study show that significant value may be added by utilizing oxygen and the natural gas infrastructure.

The fatal consequences of industrial incidents have made evident the need for suitable tools to develop inherently safer process design options. Traditionally, in a process design project, the evaluation of safety aspects is left for analysis after the detailed design has been completed. This approach leads to the use of control loops, barriers and protection layers as the only ways to prevent incidents and to reduce the possible outcomes. An alternative to this approach is the application of the concept of inherent safety, which was introduced to set up several principles that aim to enhance process safety by eliminating, avoiding or minimizing sources of risk. In this work, we present a comparison of different safety metrics in their role to evaluate the risk associated with a given process design. The indices selected for consideration are the Dow’s fire and explosion index (F&EI), the fire and explosion damage index (FEDI), the process route index (PRI) and the process stream index (PSI). All these indices use different input information and their outcomes have different rankings. The metrics were applied to an ethylene production process from shale gas to identify hazard levels, and the location of streams and pieces of equipment that pose the highest hazard within the process. An evaluation of the indices in their capability to track design changes in operating conditions aiming to improve the safety level of the process was developed. To perform the assessment of the safety metrics in a more extensive manner, an uncertainty analysis based on a Monte Carlo simulation framework was implemented and compared to the traditional use of single-value design variables. Within this context, an insightful assessment of uncertainty’s effect on process safety characteristics was achieved because of the identification of ranges of safety-relevant performance outcomes (zones of risks and opportunities) that can be probabilistically characterized. The application of the approach to the ethylene process showed how some indexes are better suited to capture the hazard characteristics associated with the process when changes in the operating conditions of most hazardous section were implemented. The methodology can be extended to other processes of interest, and may serve as a basis for the safety and process design community to propose adjustments in the structure of safety indices based on a better understanding of their performance and reliability as part of broader efforts towards their continuous improvement and refinement.

Flammable and explosive hazards, which have been well studied, are major safety concerns in industrial processes. However, the liquid aerosolization phenomenon, which increases the fire and explosion hazard of bulk liquids, has not been widely recognized. This work aims at identifying the contributors influencing liquid aerosol flammability and solving their data deficiencies by developing quantitative structure − property relationship (QSPR) models. 1215 liquid chemicals and 14 predictors have been input to train the developed machine learning models via k-fold cross validation with the consideration of principal component analysis. Three rounds of model performance comparisons are conducted to find the optimal models for liquid dynamic viscosity (LDV), surface tension (ST), and liquid vapor pressure (LVP). The most persuasive model for LDV is obtained by the exponential Gaussian process regression (GPR) approach with seven principal components, while the Matern 5/2 GPR algorithm is the most robust model for predicting ST and LVP. Due to their good interpretation and prediction performance, the optimized machine learning models can be used to predict flammability-leading properties for aerosols and can therefore help design inherently safer processes involving potential liquid aerosolization.

This work introduces a systematic approach for the optimization of an integrated system to produce fuels and simultaneously capture emissions while accounting for fairness schemes and sustainability metrics. The proposed fair-sustainable approach is relevant because a fair allocation of resources is key for long-term sustainability. The considered stakeholders in the integrated system include refineries, biorefineries, and eco-industries (taken in this work as endeavors for forest plantations resulting in capturing emissions). The optimal allocation of income among these stakeholders is analyzed by evaluating different fairness schemes (social welfare, Rawlsian welfare, and Nash approaches) and a sustainability metric (sustainability weighted return on investment). To incentivize the installation of eco-industries, economic compensation options are included. The applicability of the optimization formulation is demonstrated using a case study of Mexico. However, the model is general and can be applied to different case studies. The results show distinct income allocations under the fairness schemes and the sustainability metric. These allocations are compared to their utopic points. Additionally, the avoided emissions and the generated jobs obtained through the distinct schemes are analyzed. The results also show that the Nash scheme can provide fair trade-offs among the stakeholders’ income and environmental and social functions.

Discharge of industrial wastes constitutes one of the primary threats to the environment. A sustainable approach to handling industrial wastes is the adoption of integration and monetization scheme...

Traditional analysis of a proposed process design uses average input values in the performance assessment model, thereby generating single-point estimates. The resulting estimates ignore reliability, availability, and maintainability (RAM) considerations, or assume a fixed value based on prior experience. As a result, a probabilistic view of the impact of equipment unavailability on process profitability is not considered. Recent works have proposed a financial framework for incorporating safety and sustainability considerations in the analysis of proposed designs. Based on this research, we propose a framework to integrate RAM aspects during the conceptual design stage in a probabilistic manner using Monte Carlo simulation. Subsequently, full distribution profiles of key process performance indicators are generated, including system and section availability, annual net profit, and return on investment (ROI). Probabilistic characterization of equipment availability also facilitates the prediction of potential safety and sustainability issues, as more frequent process upsets may result in increased flaring and other potential negative consequences. A modified availability metric, using restoration instead of repair times, is used in this work to obtain a more accurate view of expected downtime and thus its effects on profitability. A propane dehydrogenation (PDH) process system is used to demonstrate the application and benefits of the framework. The proposed approach allows designers and decision-makers to comprehensively assess the impacts of equipment RAM characteristics on process availability and economic performance.

Process intensification (PI) is a design concept that offers innovative solutions for making a substantial improvement in terms of cost, energy efficiency, emission, environmental footprint, proces...

The advent of unconventional oil and gas has resulted in a plethora of shale gas production, and it has been predicted to increase from 1 in 2000 to 40% in 2035 of the total US domestic gas produced. Since shale gas is both industrially economical and environmentally clean compared to oil or coal as a resource, many studies are focused on developing technologies to monetize shale gas. However, one of the key challenges in utilizing shale gas is its varied flow rate and uncertain compositional behavior. The flow rate of a shale gas well dwindles over a period of time, and the composition differs from well to well in the same shale play. This provides a challenge in designing a plant of optimum capacity for shale gas processing and natural gas liquids (NGL) recovery. In this study, the periodic variation in feed flow rate is addressed while designing shale gas processing and NGL recovery plant. Different shale gas flow rates as obtained from average shale gas rate declination curve of a shale play over the period of well life is used for the design of five different plants with varied capacities. The corresponding economics of different plants are compared to find optimal plant capacity. The base case scenario is further improved through incorporation of heat integration and optimization of individual units of the process using pinch and sensitivity analyses, respectively. Economic analysis of the optimum plant size is also performed with a constant feed flow rate over its entire life. From the economic analysis of various period studies, it is observed that the optimum plant design with highest ROI percentage has more than minimum ROI requirement both with declining and constant flow rate case, implying that the proposed processing plant is economically viable to implement. Finally, environmental impact of the base case and improved design is also discussed.

Usually, heat exchanger network (HEN) problems focus on the design of networks that achieve full energy integration. In this work, we analyze the HEN design problem by considering fully integrated ...

Lignin is the most abundant aromatic biopolymer on Earth, and its aromatic structure makes it a promising platform for the production of biobased chemicals and other valuable building blocks. The v...

As global exploration of shale gas reserves increases, there is a need for an accurate and efficient approach to proper water management, which is one of the vital problems related to shale gas pro...

In this work, a detailed techno-economic assessment is carried out for methanol synthesis process that utilizes Cu/ZnO catalyst. First, a base-case plant is designed using a commercial methanol process. The economic feasibility study was developed to identify the potential performance metrics of the future catalytic systems and the boundaries upon which the catalyst could impact the profitability of the methanol process. In particular, the performance indicators investigated in this work look at the case of higher per-pass conversion (by introduction of higher efficiency catalysts), and the potential of utilizing low pressure reactors that could minimize compression costs. The results of this work indicated that synthesizing a catalyst with higher per-pass conversion has no significant effect on the process profitability. However, reducing the methanol synthesis reaction pressure could lead to a substantial improvement in the profitability of the plant. Insights obtained from this work can be used as the basis for identifying the characteristics of the future catalytic systems that have the potential to significantly decrease the costs associated with the conventional methanol synthesis plants.

This paper presents a systematic approach for the synthesis, selection, design, and optimization of methanol plants. A metaheuristic optimization approach is proposed to reconcile the economic and ...

Process integration (PI) is defined as “ a holistic approach to design and operation that emphasi s es the unity of the process .” PI has been used primarily to improve the efficiency of process plants, thereby improving profitability while reducing resource consumption and waste generation. These are core objectives for a sustainable industry. As such, PI is an effective framework for systematically generating sustainable strategies for the design and operation of industrial facilities. In this article, various PI tools developed for sustainable industrial processes are presented. These tools are meant to conserve natural resources, reduce emissions and wastes, and enhance process efficiency in order to improve sustainability.

Emphasis on clean energy has led to a widespread focus on lithium-ion batteries. However, a major obstacle is their degradation with several cycles or calendar aging. Battery Management System relies on an essential model-based algorithm to protect the battery from operating outside the safety limit. Thus, this work attempts to answer important research questions on battery models: (1) Are physics-based electrochemical models (EM) robust enough to identify internal cell degradation and abnormal battery behavior? (2) How are the structural simplifications and mathematical order reductions imposed on the EMs and what are their trade-offs? (3) How to apply simplified EM for safer and more efficient battery operation? (4) What are the prospects and obstacles of employing EM-based algorithms in the future? This paper presents a detailed analysis of EM from a modeling and application perspective. The paper introduces battery operating mechanisms, typical failures, and their effects. Followed by an analysis of full order EM (Pseudo 2-Dimensional), and further classification into simpler and advanced reduced-order models. The study demonstrates the gaps in theoretical understanding and their implementation for real-time battery operations such as in thermal management, energy utilization, and fault diagnosis and prognosis.

The process design and technology selection are driven by multiple objectives including technical, economic, environmental, safety, and resilience drivers. Traditionally, techno-economic analyses h...