Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and neural networks, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that neural networks are more robust modeling techniques than PCA and KPCA for the sake of fault detection.
{"title":"Advanced data-driven fault detection in gas-to-liquid plants","authors":"Nour Basha , Radhia Fezai , Byanne Malluhi , Khaled Dhibi , Gasim Ibrahim , Hanif A. Choudhury , Mohamed S. Challiwala , Hazem Nounou , Nimir Elbashir , Mohamed Nounou","doi":"10.1016/j.compchemeng.2025.109098","DOIUrl":"10.1016/j.compchemeng.2025.109098","url":null,"abstract":"<div><div>Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and neural networks, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that neural networks are more robust modeling techniques than PCA and KPCA for the sake of fault detection.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109098"},"PeriodicalIF":3.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minimizing hydrogen consumption while maintaining the production quality in the refinery is increasingly important with more usage of heavy crude oil. However, the uncertainty of the impurity content in the input flow has led to the optimal solution losing efficacy. Therefore, a synchronous optimization framework for the hydrogen network and the production system is proposed. In this work, the relationship between the production state and the hydrogen demand is characterized by a hybrid model. Besides, a Wasserstein distributionally robust optimization module is inserted into the optimization of the hydrogen network, considering the uncertain condition of the impurity content in the input flow. The results show that the balance of hydrogen consumption and production quality could be improved. a lower hydrogen demand, reduced energy consumption, and higher product profit could be achieved with a stabler production state.
{"title":"A synchronous data-driven hybrid framework for optimizing hydrotreating units and hydrogen networks under uncertainty","authors":"Shizhao Chen , Xin Peng , Chenglin Chang , Zhi Li , Weimin Zhong","doi":"10.1016/j.compchemeng.2025.109050","DOIUrl":"10.1016/j.compchemeng.2025.109050","url":null,"abstract":"<div><div>Minimizing hydrogen consumption while maintaining the production quality in the refinery is increasingly important with more usage of heavy crude oil. However, the uncertainty of the impurity content in the input flow has led to the optimal solution losing efficacy. Therefore, a synchronous optimization framework for the hydrogen network and the production system is proposed. In this work, the relationship between the production state and the hydrogen demand is characterized by a hybrid model. Besides, a Wasserstein distributionally robust optimization module is inserted into the optimization of the hydrogen network, considering the uncertain condition of the impurity content in the input flow. The results show that the balance of hydrogen consumption and production quality could be improved. a lower hydrogen demand, reduced energy consumption, and higher product profit could be achieved with a stabler production state.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109050"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.compchemeng.2025.109083
Dayron Chang Dominguez , An Phuc Dam , Shaun M. Alia , Thomas Richter , Kai Sundmacher
Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.
{"title":"Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions","authors":"Dayron Chang Dominguez , An Phuc Dam , Shaun M. Alia , Thomas Richter , Kai Sundmacher","doi":"10.1016/j.compchemeng.2025.109083","DOIUrl":"10.1016/j.compchemeng.2025.109083","url":null,"abstract":"<div><div>Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109083"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.compchemeng.2025.109085
Christoforos Brozos , Jan G. Rittig , Elie Akanny , Sandip Bhattacharya , Christina Kohlmann , Alexander Mitsos
Surfactants are key ingredients in various industries such as personal and home care with the critical micelle concentration (CMC) being of major interest. Predictive models for CMC of pure surfactants have been developed based on recent ML methods, however, in practice surfactant mixtures are typically used due to performance, environmental, and cost reasons. Herein, we develop a graph neural network (GNN) framework for surfactant mixtures to predict the temperature-dependent CMC. We collect data for 108 surfactant binary mixtures, to which we add data for pure species from our previous work Brozos et al. (2024). We then develop and train GNNs and evaluate their accuracy across different prediction test scenarios for binary mixtures relevant to practical applications. The final GNN models demonstrate very high predictive performance when interpolating between different mixture compositions and for new binary mixtures with known species. Extrapolation to binary surfactant mixtures where either one or both surfactant species are not seen before, yields accurate results for the majority of surfactant systems. We further find superior accuracy of the GNN over a semi-empirical model based on activity coefficients, which has been widely used to date. We then explore if GNN models trained solely on binary mixture and pure species data can also accurately predict the CMCs of ternary mixtures. Finally, we experimentally measure the CMC of 4 commercial surfactants that contain up to four species and industrial relevant mixtures and find a very good agreement between measured and predicted CMC values.
{"title":"Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks","authors":"Christoforos Brozos , Jan G. Rittig , Elie Akanny , Sandip Bhattacharya , Christina Kohlmann , Alexander Mitsos","doi":"10.1016/j.compchemeng.2025.109085","DOIUrl":"10.1016/j.compchemeng.2025.109085","url":null,"abstract":"<div><div>Surfactants are key ingredients in various industries such as personal and home care with the critical micelle concentration (CMC) being of major interest. Predictive models for CMC of pure surfactants have been developed based on recent ML methods, however, in practice surfactant mixtures are typically used due to performance, environmental, and cost reasons. Herein, we develop a graph neural network (GNN) framework for surfactant mixtures to predict the temperature-dependent CMC. We collect data for 108 surfactant binary mixtures, to which we add data for pure species from our previous work Brozos et al. (2024). We then develop and train GNNs and evaluate their accuracy across different prediction test scenarios for binary mixtures relevant to practical applications. The final GNN models demonstrate very high predictive performance when interpolating between different mixture compositions and for new binary mixtures with known species. Extrapolation to binary surfactant mixtures where either one or both surfactant species are not seen before, yields accurate results for the majority of surfactant systems. We further find superior accuracy of the GNN over a semi-empirical model based on activity coefficients, which has been widely used to date. We then explore if GNN models trained solely on binary mixture and pure species data can also accurately predict the CMCs of ternary mixtures. Finally, we experimentally measure the CMC of 4 commercial surfactants that contain up to four species and industrial relevant mixtures and find a very good agreement between measured and predicted CMC values.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109085"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-09DOI: 10.1016/j.compchemeng.2025.109081
Sahil Kulkarni , Benjamin Lin , Ravi Radhakrishnan
This study presents a multiscale modeling framework for simulating and predicting the behavior and biodistribution of nanoparticles (), focusing on applications such as targeted drug delivery. The framework encompasses two coupled models: (1) a DeepONet-enabled Fokker–Planck equation to model the NP drift–diffusion in the red-blood cell-free layer (RBCFL) that predicts NP margination and concentration profiles taking hematocrit and vessel radius as inputs, built on top of a hemorheological model of shear-induced blood flow and (2) a physiologically based pharmacokinetic (PBPK) model that uses the predicted concentration profiles in microvasculature to inform the biodistribution of NPs across different organ in the body.
{"title":"Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics","authors":"Sahil Kulkarni , Benjamin Lin , Ravi Radhakrishnan","doi":"10.1016/j.compchemeng.2025.109081","DOIUrl":"10.1016/j.compchemeng.2025.109081","url":null,"abstract":"<div><div>This study presents a multiscale modeling framework for simulating and predicting the behavior and biodistribution of nanoparticles (<span><math><mi>NPs</mi></math></span>), focusing on applications such as targeted drug delivery. The framework encompasses two coupled models: (1) a DeepONet-enabled Fokker–Planck equation to model the NP drift–diffusion in the red-blood cell-free layer (<strong>RBCFL</strong>) that predicts NP margination and concentration profiles taking hematocrit and vessel radius as inputs, built on top of a hemorheological model of shear-induced blood flow and (2) a physiologically based pharmacokinetic (PBPK) model that uses the predicted concentration profiles in microvasculature to inform the biodistribution of NPs across different organ in the body.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109081"},"PeriodicalIF":3.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1016/j.compchemeng.2025.109086
Akshay U․ Shenoy, Uday V․ Shenoy
A novel function-condition product (FCP) approach, where conditions are evaluated using Boolean logic, is proposed for pinch analysis targeting with two distinct advantages. First, a direct targeting formula with Boolean expressions coerced to numeric equivalents provides a superior alternative to a multi-step targeting algorithm with surplus/deficit resource loads cascaded across intervals. Second, the targeting formula allows direct calculation at any level to generate even a nonlinear grand composite curve (GCC) rather than a piecewise-linear GCC with constant slope segments within each interval. The FCP approach is initially developed for the valuation of financial derivatives (specifically, options), where the payoff and P&L (profit and loss) diagrams for option strategies at expiry are shown to be analogs of piecewise-linear GCCs. The pre-expiry P&L curves for options valued by the Nobel prize-winning Black-Scholes-Merton (BSM) model are then shown to be analogous to nonlinear GCCs. An FCP formula for targeting the minimum utilities in heat exchanger networks (HENs) and the optimum mass separating agent flowrates in mass exchanger networks (MENs) is finally derived based on formally demonstrating that each stream in a HEN / MEN is equivalent to a spread in an option strategy. To illustrate various aspects of the new methodology, examples of a crude oil option strategy (for a bull put spread, put ratio spread and butterfly spread), of HENs for both constant and variable specific heat capacity Cp, and of a reactive MEN with a general nonlinear equilibrium function are considered in detail.
{"title":"Nonlinear pinch analysis targeting inspired by options valuation and Black-Scholes-Merton model","authors":"Akshay U․ Shenoy, Uday V․ Shenoy","doi":"10.1016/j.compchemeng.2025.109086","DOIUrl":"10.1016/j.compchemeng.2025.109086","url":null,"abstract":"<div><div>A novel function-condition product (FCP) approach, where conditions are evaluated using Boolean logic, is proposed for pinch analysis targeting with two distinct advantages. First, a direct targeting formula with Boolean expressions coerced to numeric equivalents provides a superior alternative to a multi-step targeting algorithm with surplus/deficit resource loads cascaded across intervals. Second, the targeting formula allows direct calculation at any level to generate even a nonlinear grand composite curve (GCC) rather than a piecewise-linear GCC with constant slope segments within each interval. The FCP approach is initially developed for the valuation of financial derivatives (specifically, options), where the payoff and P&L (profit and loss) diagrams for option strategies at expiry are shown to be analogs of piecewise-linear GCCs. The pre-expiry P&L curves for options valued by the Nobel prize-winning Black-Scholes-Merton (BSM) model are then shown to be analogous to nonlinear GCCs. An FCP formula for targeting the minimum utilities in heat exchanger networks (HENs) and the optimum mass separating agent flowrates in mass exchanger networks (MENs) is finally derived based on formally demonstrating that each stream in a HEN / MEN is equivalent to a spread in an option strategy. To illustrate various aspects of the new methodology, examples of a crude oil option strategy (for a bull put spread, put ratio spread and butterfly spread), of HENs for both constant and variable specific heat capacity <em>C<sub>p</sub></em>, and of a reactive MEN with a general nonlinear equilibrium function are considered in detail.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109086"},"PeriodicalIF":3.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-03DOI: 10.1016/j.compchemeng.2025.109088
Assem Abdurakhmanova , Ibrahim Dincer
In accordance with the analysis utilizing the GREET datasets, this study establishes three distinct options as a foundational framework for comparing Hydrogen Pentoxide and Hydrogen Peroxide, particularly examining their potential applications for transportation sector. These options are structured based on three primary sources: electricity, natural gas and gaseous hydrogen. The variations in values across these options are attributed to electricity to simplify performance evaluation within the overall framework. These options include various methodologies for electricity generation, covering both non-renewable energy sources, such as coal, natural gas, and oil, and renewable energy sources, such as solar and biomass. A traditional method for producing Hydrogen Peroxide and Hydrogen Pentoxide is also included for comparison. As demonstrated in Option 1 (with coal) for Hydrogen Peroxide, the emission indices are 1.3 kg of CO₂ and 3.21 g of CH₄.
{"title":"Utilization of Hydrogen Pentoxide and Hydrogen Peroxide in transportation sector: A comprehensive assessment study","authors":"Assem Abdurakhmanova , Ibrahim Dincer","doi":"10.1016/j.compchemeng.2025.109088","DOIUrl":"10.1016/j.compchemeng.2025.109088","url":null,"abstract":"<div><div>In accordance with the analysis utilizing the GREET datasets, this study establishes three distinct options as a foundational framework for comparing Hydrogen Pentoxide and Hydrogen Peroxide, particularly examining their potential applications for transportation sector. These options are structured based on three primary sources: electricity, natural gas and gaseous hydrogen. The variations in values across these options are attributed to electricity to simplify performance evaluation within the overall framework. These options include various methodologies for electricity generation, covering both non-renewable energy sources, such as coal, natural gas, and oil, and renewable energy sources, such as solar and biomass. A traditional method for producing Hydrogen Peroxide and Hydrogen Pentoxide is also included for comparison. As demonstrated in Option 1 (with coal) for Hydrogen Peroxide, the emission indices are 1.3 kg of CO₂ and 3.21 g of CH₄.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109088"},"PeriodicalIF":3.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.
{"title":"Dynamic and stochastic optimization of algae cultivation process","authors":"Sercan Kivanc , Burcu Beykal , Ozgun Deliismail , Hasan Sildir","doi":"10.1016/j.compchemeng.2025.109087","DOIUrl":"10.1016/j.compchemeng.2025.109087","url":null,"abstract":"<div><div>This study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109087"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1016/j.compchemeng.2025.109082
Andrea Isella, Davide Manca
Consistent with actual decarbonization efforts in the ammonia industry, this work addresses the process design of Power-to-Ammonia plants (i.e. industrial facilities producing “green” ammonia starting from renewable energy via water electrolysis) by introducing an innovative methodology based on the multi-objective optimization of the “three pillars of sustainability”: economic, environmental, and social. Specifically, the proposed criterion performs a brute-force but exhaustive search evaluating the sizes and operating schedules of key process sections characterizing Power-to-Ammonia facilities (e.g., the renewable power plant, the electrolyzer, electricity and hydrogen storage systems, etc.) to harmonize the three pillars (which are most often conflicting) as much as possible and identify the process configuration achieving the maximum attainable global sustainability. Indeed, thanks to the scalarization technique, the proposed methodology combines the three different objective functions into a global one by an appropriate set of user-assigned weights reflecting the relative importance among the pillars. For instance, proposing 60 %, 30 %, and 10 % weights to the economic (ECO), environmental (ENV), and social (SOC) pillars, respectively, leads to a Power-to-Ammonia plant achieving a Global Sustainability Score equal to 93 % (ECO: Ammonia production costs = 750.40 USD/tNH3; ENV: Global Warming Potential = 0.76 tCO2eq/tNH3; SOC: Fire and Explosion Index = 141.48). Valuable insights into the conceptual design of chemical processes integrating renewable energy and the associated sustainability assessment criteria are provided, and further industrial application opportunities are discussed.
{"title":"Integrating economic, environmental, and social sustainability in Power-to-Ammonia plants: A multi-objective optimization methodology","authors":"Andrea Isella, Davide Manca","doi":"10.1016/j.compchemeng.2025.109082","DOIUrl":"10.1016/j.compchemeng.2025.109082","url":null,"abstract":"<div><div>Consistent with actual decarbonization efforts in the ammonia industry, this work addresses the process design of Power-to-Ammonia plants (<em>i.e.</em> industrial facilities producing “green” ammonia starting from renewable energy via water electrolysis) by introducing an innovative methodology based on the multi-objective optimization of the “three pillars of sustainability”: economic, environmental, and social. Specifically, the proposed criterion performs a brute-force but exhaustive search evaluating the sizes and operating schedules of key process sections characterizing Power-to-Ammonia facilities (<em>e.g.</em>, the renewable power plant, the electrolyzer, electricity and hydrogen storage systems, <em>etc.</em>) to harmonize the three pillars (which are most often conflicting) as much as possible and identify the process configuration achieving the maximum attainable global sustainability. Indeed, thanks to the scalarization technique, the proposed methodology combines the three different objective functions into a global one by an appropriate set of user-assigned weights reflecting the relative importance among the pillars. For instance, proposing 60 %, 30 %, and 10 % weights to the economic (ECO), environmental (ENV), and social (SOC) pillars, respectively, leads to a Power-to-Ammonia plant achieving a Global Sustainability Score equal to 93 % (ECO: Ammonia production costs = 750.40 USD/t<sub>NH3</sub>; ENV: Global Warming Potential = 0.76 t<sub>CO2eq</sub>/t<sub>NH3</sub>; SOC: Fire and Explosion Index = 141.48). Valuable insights into the conceptual design of chemical processes integrating renewable energy and the associated sustainability assessment criteria are provided, and further industrial application opportunities are discussed.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109082"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.1016/j.compchemeng.2025.109051
Patrizia Beraldi, Angelo Algieri, Gennaro Lavia
The growing demand for sustainable energy solutions necessitates innovative approaches that balance environmental and economic goals. This study proposes a comprehensive optimization framework for designing and managing hybrid multigeneration systems in the residential sector. The proposed system integrates renewable and non-renewable energy technologies, energy storage devices, and electric vehicle batteries, addressing bi-objective goals of cost minimization and greenhouse gas emission reduction. A case study of a residential complex in Italy demonstrates the model’s efficacy, achieving significant cost savings and emission reductions compared to conventional systems. The results highlight optimal configurations, trade-offs, and actionable insights for decision-makers. This work provides a valuable tool for accelerating the adoption of sustainable energy systems and achieving carbon-neutrality targets in residential buildings.
{"title":"Optimal design of hybrid multigeneration systems to enhance sustainability in the residential sector","authors":"Patrizia Beraldi, Angelo Algieri, Gennaro Lavia","doi":"10.1016/j.compchemeng.2025.109051","DOIUrl":"10.1016/j.compchemeng.2025.109051","url":null,"abstract":"<div><div>The growing demand for sustainable energy solutions necessitates innovative approaches that balance environmental and economic goals. This study proposes a comprehensive optimization framework for designing and managing hybrid multigeneration systems in the residential sector. The proposed system integrates renewable and non-renewable energy technologies, energy storage devices, and electric vehicle batteries, addressing bi-objective goals of cost minimization and greenhouse gas emission reduction. A case study of a residential complex in Italy demonstrates the model’s efficacy, achieving significant cost savings and emission reductions compared to conventional systems. The results highlight optimal configurations, trade-offs, and actionable insights for decision-makers. This work provides a valuable tool for accelerating the adoption of sustainable energy systems and achieving carbon-neutrality targets in residential buildings.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109051"},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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