Hydrogen fuel-cell electric vehicles (FCEVs) are a promising low-carbon alternative to gasoline vehicles (GVs). Still, concerns about the integrity of onboard high-pressure hydrogen storage systems continue to challenge their societal acceptance. This study establishes an empirically grounded safety benchmark for GVs. It applies a bottom-up probabilistic risk assessment (PRA) to quantify and compare fatality risk for FCEVs during in-vehicle storage and on-road operation. Historical highway fire and fatality data (2014–2023) from the U.S. National Fire Incident Reporting System are combined with vehicle-miles traveled statistics to derive a benchmark Lifetime Probability of Fatality (LPF) for GVs of 9.17×10⁻⁵, corresponding to a lifetime fatal fire risk of approximately 1 in 11,000 drivers. For FCEVs, an integrated framework combining Failure Modes and Effects Analysis, Fault Tree Analysis, Event Tree Analysis, and HyRAM+ consequence modeling is used to estimate driver-centric risk metrics, which are then converted to LPF. A probabilistic Safety Equivalence Index (SEI) is introduced to express FCEV risk relative to the GV benchmark. Monte Carlo simulations indicate an expected FCEV LPF of 4.26×10⁻⁴ (≈1 in 2,300), yielding a mean SEI of 3.26 with a 90% confidence interval spanning below parity (SEI ≈ 0.67) to about 9.10. High-pressure hydrogen releases (70MPa) dominate the risk profile, accounting for 84% of hazardous events and driving the upper tail of the SEI distribution. These findings suggest that, under current assumptions and designs, FCEVs do not yet achieve safety parity with GVs, but that parity is theoretically attainable under best-case component reliability. The results provide quantitative targets for improving high-pressure system reliability and support risk-informed design and regulatory decisions for hydrogen vehicle deployment.

The gas-to-liquid (GTL) process remains commercially attractive for producing ultra-clean fuels from natural gas, but it is highly energy- and carbon-intensive. Natural gas reforming is the most energy- and cost-intensive process in GTL plants; therefore, this work aimed to evaluate the role of heat integration in reducing plant energy and cost requirements and in mitigating the carbon intensity of GTL fuels and other products. The study evaluates the impact of systematic heat-integration methods on two industrial-scale (50,000 bbl/day) GTL configurations: a conventional autothermal reformer (ATR) and a novel Advanced Reformer System (ARS)-based GTL plant. The ARS exhibits a substantially larger recoverable heat inventory due to internal CO2 recycling and higher circulating mass flow, making it inherently more responsive to integration strategies. Pinch analysis and heat exchanger network synthesis were applied to both configurations, followed by a techno-economic evaluation using a 25-year discounted cash-flow model. Heat integration reduced external heating demand from 4.63 to 1.87 GW in the ATR-GTL plant case and from 9.57 to 4.39 GW in the ARS-GTL plant case, thereby significantly lowering indirect CO2 emissions in both cases. However, only the ARS-GTL configuration yields a neutral-to-negative carbon intensity after heat integration, demonstrating that efficiency gains are achieved in sequestering CO2 as solid carbon. Economically, heat integration increased the IRR of the ATR-GTL plant from 7% to 14%. In contrast, the ARS-GTL plant achieved an IRR of 24% by selling the carbon material (multi-walled carbon nanotubes (MWCNT)) at a conservative price of $10/kg. Sensitivity analysis revealed that the MWCNT market price has a dominant influence on profitability, whereas the heat exchanger capital cost has a minimal impact on the full-scale GTL plant. The results demonstrate that integrating heat with carbon-valorization reforming technology is essential to achieving both environmental and economic sustainability for GTL plants.

Recent geopolitical disruptions and extreme weather events have underscored the importance of resilience in global energy supply chains, particularly for import-dependent economies pursuing ambitious energy transition targets. These events have exposed the limitations of supply chain designs focused solely on cost minimization that lack the flexibility and redundancy required for secure operation under stress. As energy systems evolve toward higher shares of variable renewable energy and increased demand uncertainty, episodic manual re-planning becomes inadequate, highlighting the need for modeling frameworks that integrate predictive modeling, optimization, and control to enable intelligent and adaptive supply-chain design and operations under uncertainty. This work presents a comprehensive data-driven modeling and optimization framework for adaptive energy supply-chain networks under evolving demand. The framework integrates three layers: (i) a machine-learning model for demand forecasting and scenario generation; (ii) a multi-period stochastic optimization model for strategic network design and operations; and a (iii) learning layer that monitors performance metrics and triggers strategic recourse when demand patterns shift significantly. The operational stage, which acts as a learning layer, is posed as a rolling horizon control problem determining necessary recourse decisions to adapt to changes in demand patterns. An illustrative case study, encompassing multiple energy generation hubs, energy carriers and transportation modes is shown to demonstrate the applicability of the framework.

Energy systems face unprecedented challenges driven by deep uncertainty arising from evolving demand patterns, variable renewable integration, changing technology costs, and structural shifts. Building adaptive and flexible planning frameworks is becoming increasingly essential to hedge against these uncertainties, as traditional static optimization approaches that fix investment decisions in advance often struggle to adapt to rapidly evolving conditions. This paper presents an adaptive optimization framework that integrates Bayesian Neural Networks with Markov Chain Monte Carlo sampling (BNN-MCMC) for technology-specific probabilistic generation forecasting within a rolling-horizon stochastic programming formulation. Unlike conventional approaches that rely on static forecasts, the proposed methodology continuously updates scenario representations as new information becomes available, enabling investment decisions that respond more effectively to evolving system conditions. We formalize this integration through the α-BASIS framework, an adaptive extension of Bayesian Augmented Scenario Integration for Stochastic modeling, which transforms predictive distributions into probability-weighted scenarios and embeds them within a rolling-horizon stochastic programming formulation. Through an illustrative case study of the Texas energy network, we compare three planning strategies utilizing historical generation profiles of gas power plants, solar and wind farms: conventional stochastic optimization, adaptive rolling-horizon stochastic optimization, and deterministic optimization with perfect information. Results demonstrate that the adaptive rolling-horizon approach achieves total system costs only 9.7% above the perfect-information benchmark. The rolling-horizon strategy shows particular strength in incorporating updated scenario information, time-varying technology-cost trajectories, and investment timing. Overall, this work provides a practical methodology for energy planners facing deep uncertainty by bridging data-driven probabilistic forecasting with adaptive decision-making under uncertainty.

Quantitative structure-property relationship (QSPR) models are widely used for predicting molecular properties, yet they often struggle with highly complex and non-linear systems, such as ionic liquids (ILs), polymers, and supramolecular complexes. Traditional models, including machine learning approaches like deep neural networks (DNNs) and random forest regressors (RFRs), rely on predefined descriptors, which limit their ability to generalize across diverse chemical datasets. To address these limitations, we propose a transformer-based approach to QSPR modeling. Transformers, capture long-range dependencies and model nonlinear relationships by directly learning from raw molecular data without predefined features. In this study, we applied a transformer architecture to predict the melting points of 902 ionic liquids, a highly diverse and complex chemical system. Our model achieved excellent predictive performance, with an R2 score of 0.98 across the entire dataset, a root mean square error of 14.42 K, and robust generalizability (R2 of 0.912 on test data). Compared to traditional QSPR methods, our transformer model demonstrated superior accuracy and generalization capabilities, making it a powerful tool for predicting properties in complex molecular systems.

The CACHE Design Task Force has conducted a comprehensive, year-long study on the teaching of chemical product design across global chemical engineering programs. This paper reviews existing literature and highlights distinctions between product and process design, emphasizing the predominance of process design education in universities. Drawing from co-author contributions and responses to a widely distributed questionnaire, we present recent teaching methodologies for chemical product design. The paper discusses trends in chemical engineering diversification and the gradual inclusion of diverse applications in curricula. It concludes with a call to action for chemical engineering educators to integrate well-established product design strategies into undergraduate programs and reflects on insights shared during the 2024 FOCAPD Conference.

Decarbonizing the industrial sector is essential to achieving net-zero targets and ensuring a sustainable future. Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYN) are a set of interconnected hydrocarbon-processing plants that optimize the synergistic use of mass and energy resources in pursuit of both environmental objectives and profitability enhancement. However, this interconnectedness also introduces fragility, arising from technical and administrative dependencies among the participating facilities. In this work, a systematic framework is introduced to incorporate resilience assessment and sustainability enhancement within CHOSYNs. A CHOSYN representation is developed for a proposed industrial cluster, where processes are linked through interceptor units, which facilitate the exchange and conversion of carbon-, hydrogen-, and oxygen-based streams to meet demands. A multi-objective optimization framework is formulated with four competing goals: minimizing cost, minimizing net CO2 emissions, maximizing internal CO2 utilization, and minimizing the number of interceptors’ processing steps. The augmented ε-constraint method is used to generate a Pareto front that captures the trade-offs among these objectives. To complement the synthesis, a resilience assessment framework is applied to evaluate network performance under disruption by incorporating inter-plant dependencies and modeling disruption propagation. The results show that even under worst-case scenarios, integration through CHOSYN can achieve significant gains in CO2 utilization and reductions in raw material procurement requirements. Resilience analysis adds an important dimension by quantifying the economic impacts of disruptions to both highly connected and sparsely connected yet critical nodes, revealing vulnerabilities not evident from topology alone.

Electrifying and decarbonizing the industrial sectors are important candidate strategies towards enhancing sustainability. This work introduces a novel superstructure-based framework of an integrated system to produce low-carbon methanol via electrification and decarbonization. The building blocks include a methanol plant, CO2 capture and recycle process, solar photovoltaics (PV) modules, wind turbines, reverse osmosis, and electric boilers. The hybrid renewable energy sources of solar and wind are included to provide electric power to the entire system units while connecting to a power grid to address the diurnal fluctuations of solar and wind energy. Stranded natural gas (SNG) is used as a raw material since it is an abundant resource that is not effectively useable due to physical and economic constraints. An industrial electric boiler is considered to supply steam, heating, and cooling requirements to the methanol process. CO2 released from methanol production process is captured and recycled to reduce the environmental impact of the methanol production process. A reverse osmosis plant (RO) is employed to treat local wastewater from the SNG production and provide clean water for methanol production. An Eagle Ford site in Texas is selected as a case study. A comprehensive techno-economic analysis (TEA) and an environmental evaluation demonstrate the capability of the proposed system to produce low- carbon methanol. A multi-period mixed integer nonlinear program (MINLP) is solved to find the optimal mix of solar energy, wind energy, and local power grid to attain the maximum net annual profit. The Optimal solution was found at solar at 87.65% and wind at 12.35% with a return on investment (ROI) of 11.07%, and the payback period (PBP) is 7.6 years.

The aviation sector's dependence on high-energy-density fuels presents a challenge for decarbonization. This study evaluates the economic and environmental feasibility of retrofitting a gas-to-liquid (GTL) plant for low carbon aviation fuel (LCAF) production using solar electrification, an advanced reformer unit (CARGEN), and a hybrid configuration integrating both. The solar scenario achieved a 30% reduction in indirect emissions, lowering carbon intensity (CI) from 554.3 to 390.8 g CO2 eq/bbl, but remains economically unviable without carbon credits above $185/t. The CARGEN retrofit, which recycles CO2 into 2743 t/day multi-walled carbon nanotubes (MWCNTs), reverses net emissions and maintains strong profitability at moderate natural gas (NG) prices. The hybrid scenario achieved a net-negative total CI of -138.9 g CO2 eq/bbl, surpassing Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) targets and emerging as the most effective decarbonization strategy. Techno-economic analysis revealed that the LCAF production costs are highly sensitive to NG price fluctuations. At $3.36/MMBtu, the levelized cost of fuel (LCOF) ranges from $76.1/bbl (base case) to $257.8/bbl (hybrid). The hybrid scenario, despite higher capital expenditure (CAPEX) and operating expenditure (OPEX), remains the most viable long-term strategy, ensuring economic resilience through stable CO2 reduction costs and diversified revenue streams. However, at $7/MMBtu, its payback period exceeds 12 years under the $15/kg MWCNT pricing assumption and the highest carbon credit of $185/t, highlighting the need for policy incentives to ensure commercial scalability. This study presents a scalable model for decarbonizing aviation fuel production, aligning with global sustainability goals by integrating renewable energy and advanced CO2 utilization technologies.

The valorisation of biomass residues is a challenge andValorization an opportunity. The large number of possible added value productsAdded value products and electricity that can be obtained results in a difficult product and process designProcess design problem. Integrated processes would allow fully reutilization of the residues, providing, at least, part of the utilities and intermediates are internally produced. Systematic process synthesisProcess synthesis techniques are presented to show the development of integrated processes towards circularity. In addition, with residues, building a circular economyCircular economy occurs at different levels, at process level, reusing the waste to produce the utilities and to recover nutrients that would help produce the next harvest, and along the foodFood chain, so that the different echelons of the chain are integrated towards developing a circular economyCircular economy. This chapter presents the mathematical optimization approach developed for the simultaneous process and product design and several examples are presented to show the application to the valorisation of biomass such as orange peels, wine, coffee and oil production residues, or synthesizing products from manure developing fully integrated processes. In addition, a larger scale has also been considered such as the integration of the operation of farms and agricultural exploitation defining the animal feed for the optimal integration of the different business and reducing the impact by reducing the need for inorganic fertilizers.

The mismatch in renewable energy generation potential, levelized cost and demand across different geographies highlight the potential of a future global green energy economy, through the trade of green fuels. This potential and need call for modeling frameworks to make informed decisions on energy investments, operations and regulations. In this work, we present a multi-objective optimization framework for modeling and optimizing energy transmission strategies considering different generation locations, transportation modes and often conflicting objectives of cost, environmental impact and transportation risk. An illustrative case study on supplying renewable energy to Germany demonstrates the utility of the framework across diverse options and trade-offs. Sensitivity analysis reveals that the optimal energy carrier and transmission strategy depend on distance, demand and existing infrastructure that can be re-purposed. The framework is adaptable across geographies and scales to offer actionable insights to guide investment, operational and regulatory decisions in renewable energy and hydrogen supply chains.

Ionic liquids (ILs), recognized for their low melting points and distinct properties, have emerged as eco-friendly alternatives to volatile organic compounds and are increasingly used in fields like green chemistry, electrochemical devices, and pharmaceuticals. However, predicting the melting points of ILs poses a significant challenge due to their complex non-linear intermolecular interactions. Traditional methods, like molecular dynamics simulations require high computational time and costs along with the need for specialized force fields, which have given the rise to quantitative structure property relationship models. These models offer quick predictions but often fall short in accuracy and generalizability due to them needing special formulations or additions to account for the presence of different functional groups, thus struggling to fully capture the nuances of ionic liquid behavior. This limits their predictive effectiveness across varied IL systems. To address these challenges, we developed an encoder-based transformer model tailored for accurately predicting the melting points of ILs. Leveraging advanced attention mechanisms originally devised for natural language processing, this model discerns the contextual relationships among atoms within molecules. Specifically, we initially pre-trained the model on an extensive dataset of 1.8 billion molecules, followed by finetuning on a targeted dataset of ILs. This dualphase training has allowed the model to capture complex chemical patterns and dependencies. In this work, our model achieved a remarkable R2 score of 0.98 and a mean absolute error of 10.3 K, demonstrating its superior predictive accuracy over conventional ML models. This performance not only underscores the capability of model in enhancing the practical use of ILs across industries but also demonstrates its capacity to generalize across diverse chemical datasets.

Process systems are perpetually vulnerable to disruptions from within and outside the system, as well as to uncertainties in operating parameters, all of which may adversely affect system performance. Incorporating resilience in the conceptual process design stage allows for the integration of various correlated design goals such as flexibility, availability, and ability to quickly recover during disruptions. In this work, a methodology based on the flexibility analysis is presented that provides a path to quantifying the ability of a proposed design to manage uncertainties and disruptions through a Combined Flexibility-Availability-Resilience Index (CFARI). This metric represents the likelihood that a design is feasible given the desired flexibility, availability, and resilience goals. The proposed method systematically explores the feasible space as described by the process constraints, uncertainties, and relevant disruptions through multi-parametric programming to determine this likelihood. Given a range of possible values for design variables, the CFARI can be correlated with design variables and then applied to a design optimization formulation to represent the resilience objective. Through this method, resilience is considered holistically through integration with flexibility and availability, and trade-offs with other objectives in the design stage can be explored. Case studies involving different process systems are presented to illustrate the applicability of the CFARI as a resilience metric.

The offshore wind-integrated hydrogen production system is a novel approach to producing green energy carriers and chemicals. Even with the significant environmental benefits of this approach, there are major questions and concerns regarding its safety. Understanding better how operational parameters can promote safer operations is necessary. This paper overcomes key aspects of this gap by developing a novel method to quantify the influence of operational parameters on the formation of hazardous operational environments. Focus is given to the risks associated with gas crossover. The critical operational parameters (e.g., membrane thickness, cathodic pressure, supplied power, and H-2/O-2 recombination constant) have been identified, and their contribution to leading a potential accident scenario is assessed. The proposed method enables monitoring the unsafe operational space-the combination of parameters and their synergistic effect is causing a potential scenario. The proposed method would be an important tool that facilitates remote process control and safe operation.

The new era of energy transition is significantly impacted by technological and economic advances in process technology. It is driven by the need to use renewable energy resources, reducing carbon dioxide, and other greenhouse gas emissions. Electrification has a major role to play in the energy transition, and it can contribute to renewables integration in the current energy mix while also minimizing nonpoint and distributed emissions. However, for hard-to-decarbonize sectors such as refining and chemical manufacturing processes, a dual challenge exists to simultaneously electrify and reduce emissions while supplying the continuous energy required for industrial processes, thereby highlighting the need for wider systems analysis and integration with process technology. The main objective of this work was to design a novel framework for producing green methanol while integrating renewable energy sources into the energy mix. The framework is tested for the viability of a green methanol facility in Texas. Three coastal counties in Texas with five greenhouse gas-emitting facilities were chosen for the case study. Scenarios included incorporating solar photovoltaics (PV) with battery storage, supplementing on-grid and off-grid energy production for electricity generation. Hydrogen required in the methanol production process is supplied through green hydrogen production process, through desalination of seawater, and an integrated system of nanofiltration (NF), reverse osmosis (RO), and membrane brine concentration (MBC) (to avoid coastal brine discharge) is considered. An electrolyzer for producing hydrogen and an electric boiler for providing the required heat to the process are included. The carbon capture unit is designed to accommodate carbon emitted from industrial facilities and power plants at the chosen site. A comprehensive techno-economic analysis (TEA) shows that the production cost for green methanol using the on-grid system is lower than the off-grid system due to cost of batteries. However, life cycle assessment (LCA) demonstrates that adopting an off-grid system produces nearly zero carbon emissions. The internal rate of return (IRR) for the largest methanol production facility in the on-grid case is 32%, while the off-grid case with battery integration is 24%. A sensitivity analysis shows a reduction in production costs through multiple timeline scenarios from 2025 to 2050.

The increasing global energy demand and the transition toward sustainable energy systems have highlighted the importance of energy storage technologies by ensuring efficiency, reliability, and decarbonization. This study reviews chemical and thermal energy storage technologies, focusing on how they integrate with renewable energy sources, industrial applications, and emerging challenges. Chemical Energy Storage systems, including hydrogen storage and power-to-fuel strategies, enable long-term energy retention and efficient use, while thermal energy storage technologies facilitate waste heat recovery and grid stability. Key contributions to this work are the exploration of emerging technologies, challenges in large-scale implementation, and the role of artificial intelligence in optimizing Energy Storage Systems through predictive analytics, real-time monitoring, and advanced control strategies. This study also addresses regulatory and economic barriers that hinder widespread adoption, emphasizing the need for policy incentives and interdisciplinary collaboration. The findings suggest that energy storage will be a fundamental pillar of the sustainable energy transition. Future research should focus on improving material stability, enhancing operational efficiency, and integrating intelligent management systems to maximize the benefits of these technologies for a resilient and low-carbon energy infrastructure.

Growing concerns about the security of supply and environmental impact of fossil fuels are accelerating the efforts towards designing sustainable and resilient energy systems and supply chains. While many industrialized economies face a critical shortage for meeting their ambitious energy transition targets, the differences in between levelized costs of electricity, supply and demand in between different regions demonstrate the potential for a global green energy economy through the trade of green fuels. This potential and need call for advanced mathematical models and methods that capture multiple conflicting objectives and the specific needs of decision makers to help make informed decisions on designing renewable energy supply chains. In this research, we present a novel decisionmaking framework that integrates a multi-objective optimization model related to cost, environmental impact and transportation risk with a multi-criteria decisionmaking (MCDM) method at the post-optimization stage to guide decision-makers in navigating complex supply chain planning decisions. Our approach combines a mixed-integer linear programming (MILP) model with a modified AHP with an entropy cut-off step at the post optimization stage to form a structured methodology where the evaluations of decision makers and experts are systematically incorporated to pinpoint prioritized solutions among a set of optimal solutions to guide decision-making. The proposed framework is generalizable to other complex systems requiring confident decision making where there are multiple and often conflicting objectives and trade-offs.

Risk assessment models in the oil and gas (O&G) industry has begun to transform from time-static models like fault tree, event tree or bowtie to dynamic risk assessment (DRA) models, as the latter is able to better capture the real-time-dependent risk behavior of safety barriers. Currently, most existing works on DRA in O&G industry mainly rely on event-driven data, such as failures or accident data from similar systems for risk updating. These data can be collected only when accidents or near-misses have occurred, which limits the prediction capability of the DRA model. To address this drawback, a DRA model is developed in this paper that uses condition-monitoring data for risk updating. A significant advantage of using condition-monitoring data is that the risk can be updated before accidents or failures occur, giving the operation team more time to take preventive actions. The developed approach comprises of an offline and an online phase. In the offline phase, a conventional risk assessment is performed based on fault tree models to calculate the risks at the beginning of the operation. Through the offline analysis, we can also identify the most critical safety barriers by examining their contribution to the risk indexes. Critical safety barriers are selected for condition-monitoring. In the online phase, the condition-monitoring data are used to update the reliability of the safety barriers in real-time based on a sequential Markov Chain Monte Carlo (MCMC) algorithm using a Bayesian framework. The updated reliabilities of the safety barriers are, then, used in the offline risk assessment model for a DRA. The developed approach is applied for a DRA of liquid carryover from an oil and gas separator to downstream equipment, using real-time data from the separator's safety barriers. The results show that the developed method provides a more accurate representation of the system's performance, enabling early detection of potential failures and reducing uncertainty in risk estimates. The proposed DRA model demonstrated its effectiveness in predicting failures of safety barriers in real-time, giving operational teams ample opportunity to take corrective action. This leads to improved decision-making in the O&G industry, enabling timely response to changes in risk levels. The use of condition-monitoring data enhances the accuracy of risk estimations, representing a crucial advance in DRA applications in the O&G sector.

Some of the most highly trusted and ubiquitous process simulators have solution methods that are incompatible with algorithms designed for equation-oriented optimization. The natively unconstrained Efficient Global Optimization (EGO) algorithm approximates a black-box simulation with kriging surrogate models to convert the simulation results into a reduced-order model more suitable for optimization. This work evaluates several established constraint-handling approaches for EGO to compare their accuracy, computational efficiency, and reliability using an example simulation of an amine post-combustion carbon capture process. While each approach returned a feasible operating point in the number of iterations provided, none of them effectively converged to a solution, exploring the search space without effectively exploiting promising regions. Using the product of expected improvement and probability of feasibility as next point selection criteria resulted in the best solution value and reliability. Constraining probability of feasibility while solving for the next sample point was the least likely to solve, but the solutions found were most likely to be feasible operating points.