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}
This paper develops sparse hybrid Gaussian Radial Basis Neural Networks (GRAB-NNs) for data-driven models. The proposed architectures are hidden-layered networks combining Gaussian and sigmoid hidden nodes. Efficient training algorithms are developed for solving the mixed integer nonlinear programming problem, where the optimal number of radial basis function (RBF) centers is obtained by a bidirectional branch and bound algorithm followed by optimal estimation of the coordinates of centers / widths and connection weights by minimizing the corrected Akaike Information Criterion. Algorithmic approaches are developed for exactly satisfying mass constraints both during the training and simulation problems. Sequential decomposition-based training approaches are developed by exploiting the structure of the hybrid model that facilitates use of different training algorithms for each sublayer of the hybrid structure thus leading to faster computation. The performance of the proposed network structures and training algorithms in presence / absence of constraints are evaluated for two nonlinear dynamic chemical systems.
{"title":"Mass-Constrained hybrid Gaussian radial basis neural networks: Development, training, and applications to modeling nonlinear dynamic noisy chemical processes","authors":"Angan Mukherjee , Dipendu Gupta , Debangsu Bhattacharyya","doi":"10.1016/j.compchemeng.2025.109080","DOIUrl":"10.1016/j.compchemeng.2025.109080","url":null,"abstract":"<div><div>This paper develops sparse hybrid Gaussian Radial Basis Neural Networks (GRAB-NNs) for data-driven models. The proposed architectures are hidden-layered networks combining Gaussian and sigmoid hidden nodes. Efficient training algorithms are developed for solving the mixed integer nonlinear programming problem, where the optimal number of radial basis function (RBF) centers is obtained by a bidirectional branch and bound algorithm followed by optimal estimation of the coordinates of centers / widths and connection weights by minimizing the corrected Akaike Information Criterion. Algorithmic approaches are developed for exactly satisfying mass constraints both during the training and simulation problems. Sequential decomposition-based training approaches are developed by exploiting the structure of the hybrid model that facilitates use of different training algorithms for each sublayer of the hybrid structure thus leading to faster computation. The performance of the proposed network structures and training algorithms in presence / absence of constraints are evaluated for two nonlinear dynamic chemical systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109080"},"PeriodicalIF":3.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549412","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}
Field activities performed by human operators are indispensable in process industries despite the prevalence of automation. To ensure safe and efficient plant operations, periodic training and performance assessment of field operators (FOPs) is essential. While numerous studies have focused on control room operators, relatively little attention has been directed to FOPs. Conventional training and assessment techniques for FOPs are action-based and ignore the cognitive aspects. Here, we seek to address this crucial gap in the performance assessment of FOPs. Specifically, we use eye gaze movements of FOPs to gain insights into their information acquisition patterns, a key component of cognitive behavior. As the FOPs are mobile and visit different sections of the plant, we use head-mounted eye-trackers. A major challenge in analyzing gaze information obtained from head-mounted eye trackers is that the operators’ Field of View (FoV) varies continuously as they perform different activities. Traditionally, the challenge posed by the variations in the FoV is tackled through manual annotation of the gaze on Areas of Interest (AOIs), which is knowledge- and time-intensive. Here, we propose a methodology based on Scale-Invariant-Feature-Transform to automate the AOI identification. We demonstrate our methodology with a case study involving human subjects operating a lab-scale heat exchanger setup. Our automated approach shows high accuracy (99.6 %) in gaze-AOI mapping and requires a fraction of the time, compared to manual, frame-by-frame annotation. It, therefore, offers a practical approach for performing eye tracking on FOPs, and can engender quantification of their skills and expertise and operator-specific training.
{"title":"Performance monitoring of chemical plant field operators through eye gaze tracking","authors":"Rohit Suresh , Babji Srinivasan , Rajagopalan Srinivasan","doi":"10.1016/j.compchemeng.2025.109079","DOIUrl":"10.1016/j.compchemeng.2025.109079","url":null,"abstract":"<div><div>Field activities performed by human operators are indispensable in process industries despite the prevalence of automation. To ensure safe and efficient plant operations, periodic training and performance assessment of field operators (FOPs) is essential. While numerous studies have focused on control room operators, relatively little attention has been directed to FOPs. Conventional training and assessment techniques for FOPs are action-based and ignore the cognitive aspects. Here, we seek to address this crucial gap in the performance assessment of FOPs. Specifically, we use eye gaze movements of FOPs to gain insights into their information acquisition patterns, a key component of cognitive behavior. As the FOPs are mobile and visit different sections of the plant, we use head-mounted eye-trackers. A major challenge in analyzing gaze information obtained from head-mounted eye trackers is that the operators’ Field of View (FoV) varies continuously as they perform different activities. Traditionally, the challenge posed by the variations in the FoV is tackled through manual annotation of the gaze on Areas of Interest (AOIs), which is knowledge- and time-intensive. Here, we propose a methodology based on Scale-Invariant-Feature-Transform to automate the AOI identification. We demonstrate our methodology with a case study involving human subjects operating a lab-scale heat exchanger setup. Our automated approach shows high accuracy (99.6 %) in gaze-AOI mapping and requires a fraction of the time, compared to manual, frame-by-frame annotation. It, therefore, offers a practical approach for performing eye tracking on FOPs, and can engender quantification of their skills and expertise and operator-specific training.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109079"},"PeriodicalIF":3.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593032","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 : 2025-02-25DOI: 10.1016/j.compchemeng.2025.109076
Lunan Li , Zhimin Wu , Chuan Jin
Integrating renewable sources with existing power plants represents a viable strategy for enhancing feasibility, reducing thermodynamic irreversibility, and lowering air pollution. This study employs a biomass digestion method to produce syngas, which feeds a post-combustion chamber to assist a methane-fueled Brayton cycle. An efficient heat design model is developed using the Engineering Equation Solver (EES), integrating a geothermal-powered trigeneration unit with the upper cycle to produce power, cooling, and potable water. The integrated scheme includes a flash-binary geothermal plant, a separation vessel desalination process, multi-effect desalination, and generator-absorber-heat exchange refrigeration units. Energy, exergy, and economic analyses are conducted to assess the thermodynamic and economic feasibility of the system. A multi-criteria optimization is conducted in two scenarios: power-freshwater and exergy-net present value (NPV), using an integrated Histogram Gradient Boosting Regression (HGBR) and Multi-Objective Particle Swarm Optimization (MOPSO) model. The first scenario showed a 55.37 % increase in net electricity output (2100.28 kW) and a 51.7 % improvement in freshwater generation (36.09 kg/s) compared to the base case. The optimum point revealed an exergy efficiency of 28.36 %, a total NPV of $5.703 M, and a payback period of 4.85 years. In the second scenario, an exergy efficiency of 29.52 %, an NPV of $4.41 M, and a payback period of 5.37 years are achieved. Based on the results, the first scenario demonstrates superior performance.
{"title":"Integrating a multigeneration system into a biogas-fueled gas turbine power plant for CO2 emission reduction: An efficient design and exergy-economic assessment","authors":"Lunan Li , Zhimin Wu , Chuan Jin","doi":"10.1016/j.compchemeng.2025.109076","DOIUrl":"10.1016/j.compchemeng.2025.109076","url":null,"abstract":"<div><div>Integrating renewable sources with existing power plants represents a viable strategy for enhancing feasibility, reducing thermodynamic irreversibility, and lowering air pollution. This study employs a biomass digestion method to produce syngas, which feeds a post-combustion chamber to assist a methane-fueled Brayton cycle. An efficient heat design model is developed using the Engineering Equation Solver (EES), integrating a geothermal-powered trigeneration unit with the upper cycle to produce power, cooling, and potable water. The integrated scheme includes a flash-binary geothermal plant, a separation vessel desalination process, multi-effect desalination, and generator-absorber-heat exchange refrigeration units. Energy, exergy, and economic analyses are conducted to assess the thermodynamic and economic feasibility of the system. A multi-criteria optimization is conducted in two scenarios: power-freshwater and exergy-net present value (NPV), using an integrated Histogram Gradient Boosting Regression (HGBR) and Multi-Objective Particle Swarm Optimization (MOPSO) model. The first scenario showed a 55.37 % increase in net electricity output (2100.28 kW) and a 51.7 % improvement in freshwater generation (36.09 kg/s) compared to the base case. The optimum point revealed an exergy efficiency of 28.36 %, a total NPV of $5.703 M, and a payback period of 4.85 years. In the second scenario, an exergy efficiency of 29.52 %, an NPV of $4.41 M, and a payback period of 5.37 years are achieved. Based on the results, the first scenario demonstrates superior performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109076"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529284","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 : 2025-02-23DOI: 10.1016/j.compchemeng.2025.109066
Subhadra Devi Saripalli , Rajagopalan Srinivasan
The fine chemical industry regularly develops novel products for diverse applications and produces them at scale in multi-purpose, batch processes. These processes often involve highly hazardous chemicals and reactive chemical hazards. If an unacceptable risk is identified after the production route has been finalized, it would necessitate expensive redesigns and result in suboptimal risk management strategies with significant delays in time to market. It is, therefore, desirable to consider inherent safety analysis during route selection. The traditional methods for inherent safety analysis are not directly applicable to the fine chemicals industry which have unique characteristics; specifically, they require information on a large number of properties of materials and reactions, which are not usually available for novel pathways, especially at the route selection stage. While safety data could be determined experimentally, this would be time-consuming and expensive, especially if the route were to be rejected later in the process development. In this paper, we propose a practicable methodology that addresses these important challenges unique to fine chemicals industry. Our methodology leverages chemoinformatic models, which are increasingly becoming available and reliable, to estimate material and reaction properties. Various chemoinformatic models are systematically integrated into the process development workflow so that fire, toxicity, and reactivity hazards can be estimated when necessary, thus enabling inherently safer route selection. The methodology is illustrated using an industrial case study of Boscalid manufacture. Fifty-three safety-critical properties are predicted using various chemoinformatics methods and enable the identification of safety issues at the early stages of the process lifecycle.
{"title":"A cheminformatics-based methodology to incorporate safety considerations during accelerated process development","authors":"Subhadra Devi Saripalli , Rajagopalan Srinivasan","doi":"10.1016/j.compchemeng.2025.109066","DOIUrl":"10.1016/j.compchemeng.2025.109066","url":null,"abstract":"<div><div>The fine chemical industry regularly develops novel products for diverse applications and produces them at scale in multi-purpose, batch processes. These processes often involve highly hazardous chemicals and reactive chemical hazards. If an unacceptable risk is identified after the production route has been finalized, it would necessitate expensive redesigns and result in suboptimal risk management strategies with significant delays in time to market. It is, therefore, desirable to consider inherent safety analysis during route selection. The traditional methods for inherent safety analysis are not directly applicable to the fine chemicals industry which have unique characteristics; specifically, they require information on a large number of properties of materials and reactions, which are not usually available for novel pathways, especially at the route selection stage. While safety data could be determined experimentally, this would be time-consuming and expensive, especially if the route were to be rejected later in the process development. In this paper, we propose a practicable methodology that addresses these important challenges unique to fine chemicals industry. Our methodology leverages chemoinformatic models, which are increasingly becoming available and reliable, to estimate material and reaction properties. Various chemoinformatic models are systematically integrated into the process development workflow so that fire, toxicity, and reactivity hazards can be estimated when necessary, thus enabling inherently safer route selection. The methodology is illustrated using an industrial case study of Boscalid manufacture. Fifty-three safety-critical properties are predicted using various chemoinformatics methods and enable the identification of safety issues at the early stages of the process lifecycle.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"198 ","pages":"Article 109066"},"PeriodicalIF":3.9,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562996","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 : 2025-02-22DOI: 10.1016/j.compchemeng.2025.109068
Wu Deng , Xiankang Xin , Ruixuan Song , Xinzhou Yang , Weifeng Wang , Gaoming Yu
Oil production forecasting is essential in the petroleum and natural gas sector, providing a fundamental basis for the adjustment of development plans and improving resource utilization efficiency for engineers and decision-makers. However, current deep learning models often struggle with long-term dependencies in long time series and high computational costs, limiting their effectiveness in complex time series forecasting tasks. This paper introduced the Informer model, an enhancement over the Transformer framework, to address these limitations. For evaluation and verification, the Informer model and reference models such as CNN, LSTM, GRU, CNN-GRU, and GRU-LSTM were applied to publicly available time-series datasets, and the optimal hyperparameters of the model were identified using Bayesian optimization and the hyperband algorithm (BOHB). The experimental results demonstrated that the Informer model outperformed others in computational speed, resource efficiency, and handling large-scale data, showing potential for practical applications in the future.
{"title":"A time series forecasting method for oil production based on Informer optimized by Bayesian optimization and the hyperband algorithm (BOHB)","authors":"Wu Deng , Xiankang Xin , Ruixuan Song , Xinzhou Yang , Weifeng Wang , Gaoming Yu","doi":"10.1016/j.compchemeng.2025.109068","DOIUrl":"10.1016/j.compchemeng.2025.109068","url":null,"abstract":"<div><div>Oil production forecasting is essential in the petroleum and natural gas sector, providing a fundamental basis for the adjustment of development plans and improving resource utilization efficiency for engineers and decision-makers. However, current deep learning models often struggle with long-term dependencies in long time series and high computational costs, limiting their effectiveness in complex time series forecasting tasks. This paper introduced the Informer model, an enhancement over the Transformer framework, to address these limitations. For evaluation and verification, the Informer model and reference models such as CNN, LSTM, GRU, CNN-GRU, and GRU-LSTM were applied to publicly available time-series datasets, and the optimal hyperparameters of the model were identified using Bayesian optimization and the hyperband algorithm (BOHB). The experimental results demonstrated that the Informer model outperformed others in computational speed, resource efficiency, and handling large-scale data, showing potential for practical applications in the future.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109068"},"PeriodicalIF":3.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549411","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 : 2025-02-22DOI: 10.1016/j.compchemeng.2025.109061
Dimitrios M. Fardis , Donghyun Oh , Nikolaos V. Sahinidis , Alejandro Garciadiego , Andrew Lee
Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.
{"title":"Surrogate modeling and optimization of the leaching process in a rare earth elements recovery plant","authors":"Dimitrios M. Fardis , Donghyun Oh , Nikolaos V. Sahinidis , Alejandro Garciadiego , Andrew Lee","doi":"10.1016/j.compchemeng.2025.109061","DOIUrl":"10.1016/j.compchemeng.2025.109061","url":null,"abstract":"<div><div>Critical minerals (CMs) and Rare Earth Elements (REEs) play a vital role in crucial infrastructure technologies such as renewable energy generation and batteries. Recovering them from waste materials has recently been found to significantly reduce environmental impact and supply chain costs related to these materials. In this work, we investigate surrogate modeling techniques aimed to simplify the modeling, simulation, and optimization of the leaching processes involved in CM and REE recovery flowsheets. As there is currently a lack of systematic studies on this topic, we perform extensive computational testing to ascertain which surrogate models are easier to construct and offer high predictive accuracy. Our results suggest that sparse quadratic models balance predictive accuracy and computational efficiency. Training and using these surrogates for global optimization of the leaching process requires two orders of magnitude fewer measurements and is up to four orders of magnitude faster than optimizing the original simulation using equation-oriented optimization or derivative-free optimization.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109061"},"PeriodicalIF":3.9,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509479","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}
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