Pub Date : 2025-01-09DOI: 10.1016/j.compchemeng.2024.108983
Jiyizhe Zhang , Daria Semochkina , Naoto Sugisawa , David C. Woods , Alexei A. Lapkin
Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually inevitable in experimental and chemical processes, and finding reliable solutions is challenging when the noise is unknown or significant. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian expected quantile improvement (MO-E-EQI) under noisy settings. First, the performance of MO-E-EQI is evaluated by comparing with some recent MOBO algorithms in silico with linear and log-linear heteroscedastic noise structures and different magnitudes. It is noticed that high noise can degrade the performance of MOBO algorithms. MO-E-EQI shows robust performance in terms of hypervolume-based metric, coverage metric and number of solutions on the Pareto front. Finally, MO-E-EQI is implemented in a real case to optimize an esterification reaction to achieve the maximum space-time-yield and the minimal E-factor. The algorithm identifies a clear trade-off between the two objectives.
{"title":"Multi-objective reaction optimization under uncertainties using expected quantile improvement","authors":"Jiyizhe Zhang , Daria Semochkina , Naoto Sugisawa , David C. Woods , Alexei A. Lapkin","doi":"10.1016/j.compchemeng.2024.108983","DOIUrl":"10.1016/j.compchemeng.2024.108983","url":null,"abstract":"<div><div>Multi-objective Bayesian optimization (MOBO) has shown to be a promising tool for reaction development. However, noise is usually inevitable in experimental and chemical processes, and finding reliable solutions is challenging when the noise is unknown or significant. In this study, we focus on finding a set of optimal reaction conditions using multi-objective Euclidian expected quantile improvement (MO-E-EQI) under noisy settings. First, the performance of MO-E-EQI is evaluated by comparing with some recent MOBO algorithms <em>in silico</em> with linear and log-linear heteroscedastic noise structures and different magnitudes. It is noticed that high noise can degrade the performance of MOBO algorithms. MO-E-EQI shows robust performance in terms of hypervolume-based metric, coverage metric and number of solutions on the Pareto front. Finally, MO-E-EQI is implemented in a real case to optimize an esterification reaction to achieve the maximum space-time-yield and the minimal E-factor. The algorithm identifies a clear trade-off between the two objectives.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108983"},"PeriodicalIF":3.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136740","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 : 2025-01-05DOI: 10.1016/j.compchemeng.2025.109000
Seen Ye Lim , Nishanth G. Chemmangattuvalappil , John Frederick D. Tapia , Ianatul Khoiroh , Pui Vun Chai , Lik Yin Ng
Oleochemical industry generates palm process residue during hydrogenation of fatty acids or methyl esters. This residue, comprising fatty alcohols and alkanes with overlapping boiling points, is challenging and costly to separate using conventional distillation. Efficient recovery of fatty alcohols for commercial use, while alkanes for jet fuel, lubricants, and gasoline are beneficial. A promising solution involves halogenating fatty alcohols into derivatives with distinct boiling points from alkanes, enabling efficient distillation. Thus, identifying chemical reaction pathways for fatty alcohols and halogenating agents that occurs spontaneously under optimal conditions is crucial for cost-effectiveness and sustainability. Utilizing P-graph framework with SSG + LP algorithm, 116 thermodynamically feasible pathways were generated and analyzed using Aspen Plus. The optimal pathway successfully separated C12H25OH from C14H30 and achieved a high conversion of 90.40% for C12H25Br. This pathway also produced valuable by-products such as C4H8BrOH and C5H11OH, generating higher revenue and demonstrating industrial feasibility.
{"title":"Optimal chemical reaction pathway for palm process residue recovery using Process Graph (P-graph) framework","authors":"Seen Ye Lim , Nishanth G. Chemmangattuvalappil , John Frederick D. Tapia , Ianatul Khoiroh , Pui Vun Chai , Lik Yin Ng","doi":"10.1016/j.compchemeng.2025.109000","DOIUrl":"10.1016/j.compchemeng.2025.109000","url":null,"abstract":"<div><div>Oleochemical industry generates palm process residue during hydrogenation of fatty acids or methyl esters. This residue, comprising fatty alcohols and alkanes with overlapping boiling points, is challenging and costly to separate using conventional distillation. Efficient recovery of fatty alcohols for commercial use, while alkanes for jet fuel, lubricants, and gasoline are beneficial. A promising solution involves halogenating fatty alcohols into derivatives with distinct boiling points from alkanes, enabling efficient distillation. Thus, identifying chemical reaction pathways for fatty alcohols and halogenating agents that occurs spontaneously under optimal conditions is crucial for cost-effectiveness and sustainability. Utilizing P-graph framework with SSG + LP algorithm, 116 thermodynamically feasible pathways were generated and analyzed using Aspen Plus. The optimal pathway successfully separated C<sub>12</sub>H<sub>25</sub>OH from C<sub>14</sub>H<sub>30</sub> and achieved a high conversion of 90.40% for C<sub>12</sub>H<sub>25</sub>Br. This pathway also produced valuable by-products such as C<sub>4</sub>H<sub>8</sub>BrOH and C<sub>5</sub>H<sub>11</sub>OH, generating higher revenue and demonstrating industrial feasibility.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 109000"},"PeriodicalIF":3.9,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136775","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 : 2025-01-04DOI: 10.1016/j.compchemeng.2025.108998
Hongqiu Zhu, Yixin Lv, Minghui Liu, Can Zhou
The flow of hydrogen sulfide is a crucial factor influencing the precipitation of heavy metals in acid wastewater. However, flow regulation in industrial environments often demonstrates lag. The oxidation–reduction potential (ORP) is closely linked to the flow of hydrogen sulfide. Consequently, this paper proposes an ORP prediction model that employs a double-layer improved particle swarm optimization (DLIPSO) and extreme learning machine (ELM). To overcome the limitation of particle swarm optimization (PSO) easily getting trapped in local optima, the oppositional-based learning (OBL) strategy and time-varying inertia weights are introduced to improve the search performance of the particles. Additionally, a double-layer particle swarm structure is utilized to identify the most effective combination of optimal structure and parameters for the ELM, maximizing its predictive performance. The proposed model is validated on a real dataset and compared with five other models. Experimental results indicate that the root mean square error (RMSE) of the proposed model decreased by 9.40 % to 49.76 % compared to the other models.
{"title":"An ORP prediction model for acid wastewater sulfidation process based on improved extreme learning machine","authors":"Hongqiu Zhu, Yixin Lv, Minghui Liu, Can Zhou","doi":"10.1016/j.compchemeng.2025.108998","DOIUrl":"10.1016/j.compchemeng.2025.108998","url":null,"abstract":"<div><div>The flow of hydrogen sulfide is a crucial factor influencing the precipitation of heavy metals in acid wastewater. However, flow regulation in industrial environments often demonstrates lag. The oxidation–reduction potential (ORP) is closely linked to the flow of hydrogen sulfide. Consequently, this paper proposes an ORP prediction model that employs a double-layer improved particle swarm optimization (DLIPSO) and extreme learning machine (ELM). To overcome the limitation of particle swarm optimization (PSO) easily getting trapped in local optima, the oppositional-based learning (OBL) strategy and time-varying inertia weights are introduced to improve the search performance of the particles. Additionally, a double-layer particle swarm structure is utilized to identify the most effective combination of optimal structure and parameters for the ELM, maximizing its predictive performance. The proposed model is validated on a real dataset and compared with five other models. Experimental results indicate that the root mean square error (RMSE) of the proposed model decreased by 9.40 % to 49.76 % compared to the other models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108998"},"PeriodicalIF":3.9,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136595","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 : 2025-01-04DOI: 10.1016/j.compchemeng.2025.108999
Ricardo A.de O. Lima, Reginaldo Guirardello
This study presents a discrete-time mixed-integer linear programming (MILP) model to optimize long-term maintenance turnaround scheduling in an oil refinery focused on fuel production. Refineries are complex networks of integrated process units, and maintenance turnarounds, involving temporary shutdowns for inspection and repair, can significantly disrupt production and reduce revenues. The MILP model aims to minimize these disruptions by optimizing turnaround schedules while maintaining product supply and maximizing economic performance. The model incorporates flow, labor, resource, and planning constraints, allowing for different unit groupings and scenario simulations. Key outputs include the maintenance schedule, unit utilization rates, intermediate stock levels, production, manpower, and maintenance costs. The model serves as a decision-support tool for refining managers, enabling them to plan maintenance interventions that maximize operating profit while adhering to operational constraints.
{"title":"Long term turnaround planning for an oil refinery using a MILP model","authors":"Ricardo A.de O. Lima, Reginaldo Guirardello","doi":"10.1016/j.compchemeng.2025.108999","DOIUrl":"10.1016/j.compchemeng.2025.108999","url":null,"abstract":"<div><div>This study presents a discrete-time mixed-integer linear programming (MILP) model to optimize long-term maintenance turnaround scheduling in an oil refinery focused on fuel production. Refineries are complex networks of integrated process units, and maintenance turnarounds, involving temporary shutdowns for inspection and repair, can significantly disrupt production and reduce revenues. The MILP model aims to minimize these disruptions by optimizing turnaround schedules while maintaining product supply and maximizing economic performance. The model incorporates flow, labor, resource, and planning constraints, allowing for different unit groupings and scenario simulations. Key outputs include the maintenance schedule, unit utilization rates, intermediate stock levels, production, manpower, and maintenance costs. The model serves as a decision-support tool for refining managers, enabling them to plan maintenance interventions that maximize operating profit while adhering to operational constraints.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108999"},"PeriodicalIF":3.9,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136776","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 : 2025-01-02DOI: 10.1016/j.compchemeng.2025.108997
Youquan Xu, Zhijiang Shao, Anjan K. Tula
Molecular design and process design are critical components of industrial systems. In process design, the simultaneous design of working medium molecules and process operating conditions is the most effective approach to achieve optimal system performance. In cascade refrigeration systems, multiple refrigerants are required as working mediums, making it essential to design the evaporation and condensation temperatures at each stage to optimize the system's refrigeration coefficient. Unlike most industrial systems, cascade refrigeration systems uniquely require the simultaneous design of multiple molecules, necessitating the evaluation of individual and combined molecular properties. This paper introduces an integrated product (molecule) and process design framework for cascade refrigeration systems to address these challenges. This framework leverages a machine learning model to predict molecular properties and incorporates a process design method. The effectiveness of this approach is demonstrated in two-stage and three-stage cascade refrigeration systems.
{"title":"Integrated product and process design for cascade refrigeration","authors":"Youquan Xu, Zhijiang Shao, Anjan K. Tula","doi":"10.1016/j.compchemeng.2025.108997","DOIUrl":"10.1016/j.compchemeng.2025.108997","url":null,"abstract":"<div><div>Molecular design and process design are critical components of industrial systems. In process design, the simultaneous design of working medium molecules and process operating conditions is the most effective approach to achieve optimal system performance. In cascade refrigeration systems, multiple refrigerants are required as working mediums, making it essential to design the evaporation and condensation temperatures at each stage to optimize the system's refrigeration coefficient. Unlike most industrial systems, cascade refrigeration systems uniquely require the simultaneous design of multiple molecules, necessitating the evaluation of individual and combined molecular properties. This paper introduces an integrated product (molecule) and process design framework for cascade refrigeration systems to address these challenges. This framework leverages a machine learning model to predict molecular properties and incorporates a process design method. The effectiveness of this approach is demonstrated in two-stage and three-stage cascade refrigeration systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108997"},"PeriodicalIF":3.9,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136598","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 : 2025-01-02DOI: 10.1016/j.compchemeng.2024.108992
Zawadi Mdoe, Johannes Jäschke
The robustness, degree of conservativeness, and computational efficiency in robust multi-stage MPC are affected by scenario selection. This study explores the advantages of employing multivariate data analysis, nonlinear optimization theory, and sensitivity analysis in scenario selection to reduce conservativeness and computational burden. A novel scenario selection approach is proposed, which integrates principal component analysis and sensitivity analysis, aiming to enhance computational efficiency and mitigate conservativeness in multi-stage MPC. This method advances and extends the previously quite conservative framework of sensitivity-assisted multi-stage nonlinear MPC. Assuming that the constraints are monotonic in the parameters, the approach identifies scenarios based on sensitivities along principal components derived from analyzing large process data. The optimization problem is reformulated using the principal components to determine parameter values for critical scenarios, providing a more accurate representation of the process. The efficacy of the controller is demonstrated through various numerical examples, including a detailed thermal energy storage case study, which showcases a reduction in peak heating requirements.
{"title":"Sensitivity-based scenario selection for multi-stage MPC along principal components","authors":"Zawadi Mdoe, Johannes Jäschke","doi":"10.1016/j.compchemeng.2024.108992","DOIUrl":"10.1016/j.compchemeng.2024.108992","url":null,"abstract":"<div><div>The robustness, degree of conservativeness, and computational efficiency in robust multi-stage MPC are affected by scenario selection. This study explores the advantages of employing multivariate data analysis, nonlinear optimization theory, and sensitivity analysis in scenario selection to reduce conservativeness and computational burden. A novel scenario selection approach is proposed, which integrates principal component analysis and sensitivity analysis, aiming to enhance computational efficiency and mitigate conservativeness in multi-stage MPC. This method advances and extends the previously quite conservative framework of sensitivity-assisted multi-stage nonlinear MPC. Assuming that the constraints are monotonic in the parameters, the approach identifies scenarios based on sensitivities along principal components derived from analyzing large process data. The optimization problem is reformulated using the principal components to determine parameter values for critical scenarios, providing a more accurate representation of the process. The efficacy of the controller is demonstrated through various numerical examples, including a detailed thermal energy storage case study, which showcases a reduction in peak heating requirements.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108992"},"PeriodicalIF":3.9,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136778","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 : 2024-12-31DOI: 10.1016/j.compchemeng.2024.108996
Francisco Javier López-Flores , Maritza E. Cervantes-Gaxiola , Oscar M. Hernández-Calderón , José M. Ponce-Ortega , Jesús Raúl Ortiz-del-Castillo , Eusiel Rubio-Castro
This paper presents an innovative mathematical optimization model, formulated as a Mixed-Integer Nonlinear Programming problem, for sustainable crop allocation across a set of available parcels. The model not only maximizes the economic benefits derived from crop sales but also optimizes the use of water and fertilizers, minimizes environmental impact, and assesses health and safety risks to workers using the Process Route Health Index, adapted from the chemical industry. The adaptation methodology is designed and presented step-by-step to highlight its applicability and relevance in the agricultural context. The integration of mass networks for the use, reuse, and regeneration of water and fertilizers allows for significant optimization of resources, reducing both operating costs and environmental waste. A case study involving the allocation of four crops across 12 parcels over three sowing cycles demonstrates the model's applicability under different scenarios, evaluating profits, Eco-indicator 95, water footprint, and occupational health. The results obtained demonstrate the model's ability to balance economic benefits with environmental sustainability and labor safety.
{"title":"Comprehensive crop allocation model: Balancing profitability, environmental impact, and occupational health","authors":"Francisco Javier López-Flores , Maritza E. Cervantes-Gaxiola , Oscar M. Hernández-Calderón , José M. Ponce-Ortega , Jesús Raúl Ortiz-del-Castillo , Eusiel Rubio-Castro","doi":"10.1016/j.compchemeng.2024.108996","DOIUrl":"10.1016/j.compchemeng.2024.108996","url":null,"abstract":"<div><div>This paper presents an innovative mathematical optimization model, formulated as a Mixed-Integer Nonlinear Programming problem, for sustainable crop allocation across a set of available parcels. The model not only maximizes the economic benefits derived from crop sales but also optimizes the use of water and fertilizers, minimizes environmental impact, and assesses health and safety risks to workers using the Process Route Health Index, adapted from the chemical industry. The adaptation methodology is designed and presented step-by-step to highlight its applicability and relevance in the agricultural context. The integration of mass networks for the use, reuse, and regeneration of water and fertilizers allows for significant optimization of resources, reducing both operating costs and environmental waste. A case study involving the allocation of four crops across 12 parcels over three sowing cycles demonstrates the model's applicability under different scenarios, evaluating profits, Eco-indicator 95, water footprint, and occupational health. The results obtained demonstrate the model's ability to balance economic benefits with environmental sustainability and labor safety.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108996"},"PeriodicalIF":3.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136777","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 : 2024-12-30DOI: 10.1016/j.compchemeng.2024.108987
Oscar Daniel Lara-Montaño , Fernando Israel Gómez-Castro , Claudia Gutiérrez-Antonio , Elena Niculina Dragoi
This paper presents the development of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic inspired by success attribution theory, designed to address complex, high-dimensional optimization problems. SBOA balances exploration and exploitation by utilizing high-performing solutions and average-performing candidates to guide the search process, dynamically adjusting based on solution quality. The algorithm is evaluated against seven well-established optimization methods using CEC 2017 benchmark functions in 10, 30, and 50 dimensions. It is applied to a real-world engineering problem involving the optimal design of shell-and-tube heat exchangers (STHEs). The results demonstrate that SBOA consistently surpasses most competing algorithms, especially in higher-dimensional cases, achieving lower objective values and faster convergence. Statistical analyses, including the Wilcoxon signed-rank test, confirm the significant advantages of SBOA in benchmark performance and cost-effectiveness in practical engineering applications. These findings position SBOA as a highly adaptable and efficient optimization tool for addressing complex tasks.
{"title":"Success-Based Optimization Algorithm (SBOA): Development and enhancement of a metaheuristic optimizer","authors":"Oscar Daniel Lara-Montaño , Fernando Israel Gómez-Castro , Claudia Gutiérrez-Antonio , Elena Niculina Dragoi","doi":"10.1016/j.compchemeng.2024.108987","DOIUrl":"10.1016/j.compchemeng.2024.108987","url":null,"abstract":"<div><div>This paper presents the development of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic inspired by success attribution theory, designed to address complex, high-dimensional optimization problems. SBOA balances exploration and exploitation by utilizing high-performing solutions and average-performing candidates to guide the search process, dynamically adjusting based on solution quality. The algorithm is evaluated against seven well-established optimization methods using CEC 2017 benchmark functions in 10, 30, and 50 dimensions. It is applied to a real-world engineering problem involving the optimal design of shell-and-tube heat exchangers (STHEs). The results demonstrate that SBOA consistently surpasses most competing algorithms, especially in higher-dimensional cases, achieving lower objective values and faster convergence. Statistical analyses, including the Wilcoxon signed-rank test, confirm the significant advantages of SBOA in benchmark performance and cost-effectiveness in practical engineering applications. These findings position SBOA as a highly adaptable and efficient optimization tool for addressing complex tasks.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108987"},"PeriodicalIF":3.9,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136594","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 : 2024-12-28DOI: 10.1016/j.compchemeng.2024.108994
Angel Alfaro-Bernardino, César Ramírez-Márquez, José M. Ponce-Ortega, Fabricio Nápoles-Rivera
Arsenic contamination in groundwater presents significant health risks, demanding effective treatment solutions. This study introduces a mathematical programming method to determine the optimal location to place arsenic treatment plants, select the appropriate technology, and design large-scale water distribution networks. This work focuses on minimizing costs associated with pumping, piping, plant installation, and operation while complying with the regulations of arsenic levels in drinking water. The approach involves a nonlinear mixed-integer mathematical programming model coupled with a detailed procedure to find solutions. In the implementation of this model, the study not only explores the best strategies to reduce the arsenic found in drinking water to safer levels in affected wells, but it also works to design an efficient water network. An analysis of areas with wells that show a concentration of arsenic above permissible levels demonstrates how the proposed solutions can effectively lower arsenic levels to meet safety standards and optimize water supply systems. The findings highlight the potential of significantly improving water quality and public health through strategic infrastructure, planning, and technological application.
{"title":"Optimizing arsenic removal in water supply: A mathematical approach for plant location, technology selection, and network synthesis","authors":"Angel Alfaro-Bernardino, César Ramírez-Márquez, José M. Ponce-Ortega, Fabricio Nápoles-Rivera","doi":"10.1016/j.compchemeng.2024.108994","DOIUrl":"10.1016/j.compchemeng.2024.108994","url":null,"abstract":"<div><div>Arsenic contamination in groundwater presents significant health risks, demanding effective treatment solutions. This study introduces a mathematical programming method to determine the optimal location to place arsenic treatment plants, select the appropriate technology, and design large-scale water distribution networks. This work focuses on minimizing costs associated with pumping, piping, plant installation, and operation while complying with the regulations of arsenic levels in drinking water. The approach involves a nonlinear mixed-integer mathematical programming model coupled with a detailed procedure to find solutions. In the implementation of this model, the study not only explores the best strategies to reduce the arsenic found in drinking water to safer levels in affected wells, but it also works to design an efficient water network. An analysis of areas with wells that show a concentration of arsenic above permissible levels demonstrates how the proposed solutions can effectively lower arsenic levels to meet safety standards and optimize water supply systems. The findings highlight the potential of significantly improving water quality and public health through strategic infrastructure, planning, and technological application.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108994"},"PeriodicalIF":3.9,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136596","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 : 2024-12-27DOI: 10.1016/j.compchemeng.2024.108989
Akshay Ajagekar , Benjamin Decardi-Nelson , Chao Shang , Fengqi You
Deep generative models like diffusion models have generated significant interest in computer-aided molecular design by enabling the automated generation of novel molecular structures. This manuscript aims to highlight the potential of diffusion models in computer-aided molecular design (CAMD) while addressing key limitations in their practical implementation. Diffusion models trained for specific molecular design problems can suffer for design tasks with alternate desired property requirements. To address this challenge, we provide perspectives on the integration of generative diffusion models with optimization methods for CAMD. We examine how pretrained equivariant diffusion models can be effectively aligned with text-guided molecular generation through optimization in the latent space. Computational experiments targeting drug design demonstrate the framework's capability of generating valid molecular structures that satisfy multiple objectives. This work underscores the potential of combining pretrained generative models with gradient-free optimization methods like genetic algorithms to enhance molecular design precision without incurring significant computational costs associated with finetuning diffusion models. Beyond highlighting the practical utility of diffusion models in CAMD, we identify key challenges encountered while adopting these models and propose future research directions to address them, providing a comprehensive roadmap for advancing the field of computational molecular design.
{"title":"Computer-aided molecular design by aligning generative diffusion models: Perspectives and challenges","authors":"Akshay Ajagekar , Benjamin Decardi-Nelson , Chao Shang , Fengqi You","doi":"10.1016/j.compchemeng.2024.108989","DOIUrl":"10.1016/j.compchemeng.2024.108989","url":null,"abstract":"<div><div>Deep generative models like diffusion models have generated significant interest in computer-aided molecular design by enabling the automated generation of novel molecular structures. This manuscript aims to highlight the potential of diffusion models in computer-aided molecular design (CAMD) while addressing key limitations in their practical implementation. Diffusion models trained for specific molecular design problems can suffer for design tasks with alternate desired property requirements. To address this challenge, we provide perspectives on the integration of generative diffusion models with optimization methods for CAMD. We examine how pretrained equivariant diffusion models can be effectively aligned with text-guided molecular generation through optimization in the latent space. Computational experiments targeting drug design demonstrate the framework's capability of generating valid molecular structures that satisfy multiple objectives. This work underscores the potential of combining pretrained generative models with gradient-free optimization methods like genetic algorithms to enhance molecular design precision without incurring significant computational costs associated with finetuning diffusion models. Beyond highlighting the practical utility of diffusion models in CAMD, we identify key challenges encountered while adopting these models and propose future research directions to address them, providing a comprehensive roadmap for advancing the field of computational molecular design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108989"},"PeriodicalIF":3.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136356","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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