Pub Date : 2025-01-25DOI: 10.1016/j.compchemeng.2025.109012
Q.H. Le , P. Carrera , M.C.M. van Loosdrecht , E.I.P. Volcke
While nowadays a lot of measurements are conducted at wastewater treatment plants, data reliability could further be improved, e.g., through data reconciliation. This study demonstrated the added value of data reconciliation to improve data quality in a full-scale wastewater treatment plant. Also, the effect of the mass balance setting (linear and bilinear mass balances) was quantitatively evaluated, considering data sets with missing measurements and with gross errors. The improvement in the precision of the key variables was higher with bilinear mass balances (40–80 %) compared to the linear setting (0–70 %). Besides, it delivered a higher number of improved key variables, especially when flow measurements were limited (minimum improved variables of 15 and 0, respectively). Bilinear mass balances were also more efficient in gross error detection and played a crucial role in cross-validation based on flow measurements, resulting in lower incorrectly-identified gross errors. Overall, it is recommended to use bilinear mass balances.
{"title":"Data evaluation for wastewater treatment plants: Linear vs bilinear mass balances","authors":"Q.H. Le , P. Carrera , M.C.M. van Loosdrecht , E.I.P. Volcke","doi":"10.1016/j.compchemeng.2025.109012","DOIUrl":"10.1016/j.compchemeng.2025.109012","url":null,"abstract":"<div><div>While nowadays a lot of measurements are conducted at wastewater treatment plants, data reliability could further be improved, e.g., through data reconciliation. This study demonstrated the added value of data reconciliation to improve data quality in a full-scale wastewater treatment plant. Also, the effect of the mass balance setting (linear and bilinear mass balances) was quantitatively evaluated, considering data sets with missing measurements and with gross errors. The improvement in the precision of the key variables was higher with bilinear mass balances (40–80 %) compared to the linear setting (0–70 %). Besides, it delivered a higher number of improved key variables, especially when flow measurements were limited (minimum improved variables of 15 and 0, respectively). Bilinear mass balances were also more efficient in gross error detection and played a crucial role in cross-validation based on flow measurements, resulting in lower incorrectly-identified gross errors. Overall, it is recommended to use bilinear mass balances.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109012"},"PeriodicalIF":3.9,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348533","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 : 2025-01-23DOI: 10.1016/j.compchemeng.2025.109008
Moheb Mottaghi, Saeed Mansour
Recently, lithium-ion batteries (LIBs) have achieved more acceptance as clean and sustainable technology because of their widespread application in exploiting portable electronic devices and electric vehicles (EVs). Since the LIBs have a finite useful life cycle and cannot be applied after losing their initial capacity, focusing on the end-of-life (EOL) LIBs and sustainability in the supply chain network design (SCND) of these batteries seems obligatory. In this respect, this study deploys a multi-objective stochastic robust optimization model to plan and design a sustainable closed-loop LIBs supply chain (SC) network under uncertainties considering environmental and social aspects alongside economic aspects. The effective life cycle assessment (LCA) method is incorporated to evaluate the relevant environmental impacts (EIs). Various relevant social measures are adopted in the model to calculate and formulate the social impacts. Likewise, the augmented ε-constraint method is applied to provide the Pareto optimal set. Eventually, the performance and validity of the proposed model will be vindicated by a real case study in Iran. The key finding of this paper indicates that paying attention to EOL strategies and addressing the reverse SC (RSC) increases total profits by 25.18 %. Also, the model can manage the environmental and social burdens of LIBs, particularly at the EOL stage.
{"title":"A multi-objective robust optimization model to sustainable closed-loop lithium-ion battery supply chain network design under uncertainties","authors":"Moheb Mottaghi, Saeed Mansour","doi":"10.1016/j.compchemeng.2025.109008","DOIUrl":"10.1016/j.compchemeng.2025.109008","url":null,"abstract":"<div><div>Recently, lithium-ion batteries (LIBs) have achieved more acceptance as clean and sustainable technology because of their widespread application in exploiting portable electronic devices and electric vehicles (EVs). Since the LIBs have a finite useful life cycle and cannot be applied after losing their initial capacity, focusing on the end-of-life (EOL) LIBs and sustainability in the supply chain network design (SCND) of these batteries seems obligatory. In this respect, this study deploys a multi-objective stochastic robust optimization model to plan and design a sustainable closed-loop LIBs supply chain (SC) network under uncertainties considering environmental and social aspects alongside economic aspects. The effective life cycle assessment (LCA) method is incorporated to evaluate the relevant environmental impacts (EIs). Various relevant social measures are adopted in the model to calculate and formulate the social impacts. Likewise, the augmented ε-constraint method is applied to provide the Pareto optimal set. Eventually, the performance and validity of the proposed model will be vindicated by a real case study in Iran. The key finding of this paper indicates that paying attention to EOL strategies and addressing the reverse SC (RSC) increases total profits by 25.18 %. Also, the model can manage the environmental and social burdens of LIBs, particularly at the EOL stage.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109008"},"PeriodicalIF":3.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348534","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 : 2025-01-22DOI: 10.1016/j.compchemeng.2025.109009
Teo Protoulis , Ioannis Kordatos , Ioannis Kalogeropoulos , Haralambos Sarimveis , Alex Alexandridis
In this work, we introduce a nonlinear economic-oriented model predictive control framework that can optimize the economic operation of wastewater treatment plants (WWTPs), while accounting for inlet flow disturbances. The proposed method utilizes an attention-based recurrent neural network (RNN) model to predict influent flow rate variations, and a WWTP reduced-order model specifically tailored for MPC integration. At each sampling instant, the proposed scheme recursively solves an optimal control problem, where the objective is to minimize the plant energy consumption. The inlet flow rate RNN predictions are integrated within the scheme and critical controller parameters, such as the prediction horizon, are optimized by considering the best RNN multi-step ahead prediction horizon. The proposed framework is applied to a modified benchmark simulation model no 1 (BSM1) representation that corresponds to an actual WWTP and its performance is compared against different control schemes, outperforming the alternative methods in terms of optimizing WWTP performance.
{"title":"Control of wastewater treatment plants using economic-oriented MPC and attention-based RNN disturbance prediction models","authors":"Teo Protoulis , Ioannis Kordatos , Ioannis Kalogeropoulos , Haralambos Sarimveis , Alex Alexandridis","doi":"10.1016/j.compchemeng.2025.109009","DOIUrl":"10.1016/j.compchemeng.2025.109009","url":null,"abstract":"<div><div>In this work, we introduce a nonlinear economic-oriented model predictive control framework that can optimize the economic operation of wastewater treatment plants (WWTPs), while accounting for inlet flow disturbances. The proposed method utilizes an attention-based recurrent neural network (RNN) model to predict influent flow rate variations, and a WWTP reduced-order model specifically tailored for MPC integration. At each sampling instant, the proposed scheme recursively solves an optimal control problem, where the objective is to minimize the plant energy consumption. The inlet flow rate RNN predictions are integrated within the scheme and critical controller parameters, such as the prediction horizon, are optimized by considering the best RNN multi-step ahead prediction horizon. The proposed framework is applied to a modified benchmark simulation model no 1 (BSM1) representation that corresponds to an actual WWTP and its performance is compared against different control schemes, outperforming the alternative methods in terms of optimizing WWTP performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109009"},"PeriodicalIF":3.9,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422195","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}
In this paper, we propose a computationally efficient feedback iterative learning control (ILC) scheme for nonlinear batch processes. We present a structured framework that delineates the feedback ILC as a composite of two integral components: a state feedback controller and a conventional ILC mechanism. Within this framework, we employ policy search techniques to optimize the feedback component. In parallel, we tackle the feedforward aspect by formulating a stochastic optimal ILC problem. These two components are offline iteratively updated, thereby ensuring convergence under ideal conditions. To account for missing process models in practical scenarios, we incorporate Gaussian process (GP) modeling into our framework. By leveraging the GP model, we extend our iterative optimization approach to a GP-based feedback ILC optimization algorithm that guarantees tractability. We use two numerical examples to demonstrate the merits of our framework, including its fast convergence and effective rejection of disturbances.
{"title":"A computationally efficient policy optimization scheme in feedback iterative learning control for nonlinear batch process","authors":"Kaihua Gao , Jingyi Lu , Yuanqiang Zhou , Furong Gao","doi":"10.1016/j.compchemeng.2025.109005","DOIUrl":"10.1016/j.compchemeng.2025.109005","url":null,"abstract":"<div><div>In this paper, we propose a computationally efficient feedback iterative learning control (ILC) scheme for nonlinear batch processes. We present a structured framework that delineates the feedback ILC as a composite of two integral components: a state feedback controller and a conventional ILC mechanism. Within this framework, we employ policy search techniques to optimize the feedback component. In parallel, we tackle the feedforward aspect by formulating a stochastic optimal ILC problem. These two components are offline iteratively updated, thereby ensuring convergence under ideal conditions. To account for missing process models in practical scenarios, we incorporate Gaussian process (GP) modeling into our framework. By leveraging the GP model, we extend our iterative optimization approach to a GP-based feedback ILC optimization algorithm that guarantees tractability. We use two numerical examples to demonstrate the merits of our framework, including its fast convergence and effective rejection of disturbances.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109005"},"PeriodicalIF":3.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348532","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 : 2025-01-18DOI: 10.1016/j.compchemeng.2025.109007
Aniket Chitre , Daria Semochkina , David C. Woods , Alexei A. Lapkin
Liquid formulation design involves using a relatively limited experimental budget to search a high-dimensional space, owing to the combinatorial selection of ingredients and their concentrations from a larger subset of available ingredients. This work investigates alternative shampoo formulations. A space-filling design is desired for screening relatively unexplored formulation chemistries. One of the few computationally efficient solutions for this mixed nominal-continuous design of experiments problem is the adoption of maximum projection designs with quantitative and qualitative factors (MaxProQQ). However, such purely space-filling designs can select experiments in infeasible regions of the design space. Here, stable products are considered feasible. We develop and apply weighted-space filling designs, where predictive phase stability classifiers are trained for difficult-to-formulate (predominantly unstable) sub-systems, to guide these experiments to regions of feasibility, whilst simultaneously optimising for chemical diversity by building on MaxProQQ. This approach is extendable to other mixed-variable design problems, particularly those with sequential design objectives.
{"title":"Machine learning-guided space-filling designs for high throughput liquid formulation development","authors":"Aniket Chitre , Daria Semochkina , David C. Woods , Alexei A. Lapkin","doi":"10.1016/j.compchemeng.2025.109007","DOIUrl":"10.1016/j.compchemeng.2025.109007","url":null,"abstract":"<div><div>Liquid formulation design involves using a relatively limited experimental budget to search a high-dimensional space, owing to the combinatorial selection of ingredients and their concentrations from a larger subset of available ingredients. This work investigates alternative shampoo formulations. A space-filling design is desired for screening relatively unexplored formulation chemistries. One of the few computationally efficient solutions for this mixed nominal-continuous design of experiments problem is the adoption of maximum projection designs with quantitative and qualitative factors (MaxProQQ). However, such purely space-filling designs can select experiments in infeasible regions of the design space. Here, stable products are considered feasible. We develop and apply weighted-space filling designs, where predictive phase stability classifiers are trained for difficult-to-formulate (predominantly unstable) sub-systems, to guide these experiments to regions of feasibility, whilst simultaneously optimising for chemical diversity by building on MaxProQQ. This approach is extendable to other mixed-variable design problems, particularly those with sequential design objectives.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109007"},"PeriodicalIF":3.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360858","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 : 2025-01-18DOI: 10.1016/j.compchemeng.2025.109010
Garima Singh, Sayed Golam Mohiuddin, Sreyashi Ghosh, Jenet Narzary, Mehmet A. Orman, Michael Nikolaou
A small fraction of infectious bacteria use persistence as a strategy to survive exposure to antibiotics. Pulse dosing of antibiotics, if designed well, has long been considered a potentially effective strategy towards eradication of such bacterial pathogens. In a recent study, we developed a method to systematically design optimal pulse dosing regimens for rapid eradication of persisters with -lactam antibiotics, and validated the effectiveness of that method experimentally. In this paper, we extend that method for fluoroquinolones. This is because, in contrast to -lactams, fluoroquinolones impart different dynamic behavior on treated bacteria, by inducing persister formation and by triggering a non-negligible post-antibiotic effect. Pulse dosing designed according to the proposed method demonstrated rapid bacterial population reduction compared to constant dosing, underscoring the potential of optimal pulse dosing for efficient use of fluoroquinolone antibiotics. In addition, model fitting and parameter estimation also highlighted differences in persister mechanisms between fluoroquinolones and β-lactams. Overall, our study demonstrates that pulse dosing strategies can be effectively designed with the proposed method, using simple formulas and data derived from basic experiments.
{"title":"Systematic design of pulse dosing to eradicate persister bacteria: The case of fluoroquinolones","authors":"Garima Singh, Sayed Golam Mohiuddin, Sreyashi Ghosh, Jenet Narzary, Mehmet A. Orman, Michael Nikolaou","doi":"10.1016/j.compchemeng.2025.109010","DOIUrl":"10.1016/j.compchemeng.2025.109010","url":null,"abstract":"<div><div>A small fraction of infectious bacteria use persistence as a strategy to survive exposure to antibiotics. Pulse dosing of antibiotics, if designed well, has long been considered a potentially effective strategy towards eradication of such bacterial pathogens. In a recent study, we developed a method to systematically design optimal pulse dosing regimens for rapid eradication of persisters with <span><math><mi>β</mi></math></span>-lactam antibiotics, and validated the effectiveness of that method experimentally. In this paper, we extend that method for fluoroquinolones. This is because, in contrast to <span><math><mi>β</mi></math></span>-lactams, fluoroquinolones impart different dynamic behavior on treated bacteria, by inducing persister formation and by triggering a non-negligible post-antibiotic effect. Pulse dosing designed according to the proposed method demonstrated rapid bacterial population reduction compared to constant dosing, underscoring the potential of optimal pulse dosing for efficient use of fluoroquinolone antibiotics. In addition, model fitting and parameter estimation also highlighted differences in persister mechanisms between fluoroquinolones and β-lactams. Overall, our study demonstrates that pulse dosing strategies can be effectively designed with the proposed method, using simple formulas and data derived from basic experiments.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109010"},"PeriodicalIF":3.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348530","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}
Support vector clustering (SVC) is an effective data-driven method to construct uncertainty sets in robust optimization (RO). However, it cannot appropriately address varying uncertainty in a contextually uncertain environment. In this work, we propose a new contextual RO (CRO) scheme, where an efficient contextual uncertainty set called kNN-SVC is developed to capture the correlation between covariates and uncertainty. Using the k-nearest neighbors (kNN) to select a subset of historical observations, contextual information can be integrated into SVC uncertainty sets, thereby alleviating conservatism while inheriting merits of SVC such as polytopic representability and ease of manipulating robustness. Besides, using only a fraction of data samples ensures low computational costs. Numerical examples demonstrate the performance improvement of the proposed kNN-SVC uncertainty set over conventional sets without considering contextual information. An industrial case of gasoline blending shows the usefulness of the proposed approach in producing robust decisions against linearization errors in nonlinear blending.
{"title":"Data-driven contextual robust optimization based on support vector clustering","authors":"Xianyu Li , Fenglian Dong , Zhiwei Wei , Chao Shang","doi":"10.1016/j.compchemeng.2025.109004","DOIUrl":"10.1016/j.compchemeng.2025.109004","url":null,"abstract":"<div><div>Support vector clustering (SVC) is an effective data-driven method to construct uncertainty sets in robust optimization (RO). However, it cannot appropriately address varying uncertainty in a contextually uncertain environment. In this work, we propose a new contextual RO (CRO) scheme, where an efficient contextual uncertainty set called kNN-SVC is developed to capture the correlation between covariates and uncertainty. Using the k-nearest neighbors (kNN) to select a subset of historical observations, contextual information can be integrated into SVC uncertainty sets, thereby alleviating conservatism while inheriting merits of SVC such as polytopic representability and ease of manipulating robustness. Besides, using only a fraction of data samples ensures low computational costs. Numerical examples demonstrate the performance improvement of the proposed kNN-SVC uncertainty set over conventional sets without considering contextual information. An industrial case of gasoline blending shows the usefulness of the proposed approach in producing robust decisions against linearization errors in nonlinear blending.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109004"},"PeriodicalIF":3.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348529","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 : 2025-01-17DOI: 10.1016/j.compchemeng.2024.108993
Daniel Ovalle , David A. Liñán , Albert Lee , Jorge M. Gómez , Luis Ricardez-Sandoval , Ignacio E. Grossmann , David E. Bernal Neira
Optimization of chemical processes is challenging due to nonlinearities arising from chemical principles and discrete design decisions. The optimal synthesis and design of chemical processes can be posed as a Generalized Disjunctive Programming (GDP) problem. While reformulating GDP problems as Mixed-Integer Nonlinear Programming (MINLP) problems is common, specialized algorithms for GDP remain scarce. This study introduces the Logic-Based Discrete-Steepest Descent Algorithm (LD-SDA) as a solution method for GDP problems involving ordered Boolean variables. LD-SDA transforms these variables into external integer decisions and uses a two-level decomposition: the upper-level sets external configurations, and the lower-level solves the remaining variables, efficiently exploiting the GDP structure. In the case studies presented in this work, including batch processing, reactor superstructures, and distillation columns, LD-SDA consistently outperforms conventional GDP and MINLP solvers, especially as the problem size grows. LD-SDA also proves superior when solving challenging problems where other solvers encounter difficulties finding optimal solutions.
{"title":"Logic-Based Discrete-Steepest Descent: A solution method for process synthesis Generalized Disjunctive Programs","authors":"Daniel Ovalle , David A. Liñán , Albert Lee , Jorge M. Gómez , Luis Ricardez-Sandoval , Ignacio E. Grossmann , David E. Bernal Neira","doi":"10.1016/j.compchemeng.2024.108993","DOIUrl":"10.1016/j.compchemeng.2024.108993","url":null,"abstract":"<div><div>Optimization of chemical processes is challenging due to nonlinearities arising from chemical principles and discrete design decisions. The optimal synthesis and design of chemical processes can be posed as a Generalized Disjunctive Programming (GDP) problem. While reformulating GDP problems as Mixed-Integer Nonlinear Programming (MINLP) problems is common, specialized algorithms for GDP remain scarce. This study introduces the Logic-Based Discrete-Steepest Descent Algorithm (LD-SDA) as a solution method for GDP problems involving ordered Boolean variables. LD-SDA transforms these variables into external integer decisions and uses a two-level decomposition: the upper-level sets external configurations, and the lower-level solves the remaining variables, efficiently exploiting the GDP structure. In the case studies presented in this work, including batch processing, reactor superstructures, and distillation columns, LD-SDA consistently outperforms conventional GDP and MINLP solvers, especially as the problem size grows. LD-SDA also proves superior when solving challenging problems where other solvers encounter difficulties finding optimal solutions.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 108993"},"PeriodicalIF":3.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348531","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}
Nowadays, the management of natural gas networks primarily relies on the expertise gained by operators over the years. Nevertheless, the need to reduce energy consumption and the progressive installation of electric compressors call for the adoption of systematic optimization tools. This study proposes a Mixed Integer Linear Programming (MILP) model for optimizing the operation of complex real-world gas networks to minimize the environmental impact of the compression work in presence of both gas-turbine driven and electric compressors. The operational problem includes the gas transport dynamic equations, detailed modeling of compressor stations and control valves, while handling complex branch and looped networks with possible reverse flow. To address large-scale problems, a graph reduction procedure and a novel bilevel decomposition algorithm are developed. This methodology, validated with real data, enables the optimization of the nationwide Italian network, comprising 51 compressors and 9727 km of pipes.
{"title":"A detailed MILP model and an ad hoc decomposition algorithm for the operational optimization of gas transport networks","authors":"Lavinia Marina Paola Ghilardi , Francesco Casella , Daniele Barbati , Roberto Palazzo , Emanuele Martelli","doi":"10.1016/j.compchemeng.2025.109006","DOIUrl":"10.1016/j.compchemeng.2025.109006","url":null,"abstract":"<div><div>Nowadays, the management of natural gas networks primarily relies on the expertise gained by operators over the years. Nevertheless, the need to reduce energy consumption and the progressive installation of electric compressors call for the adoption of systematic optimization tools. This study proposes a Mixed Integer Linear Programming (MILP) model for optimizing the operation of complex real-world gas networks to minimize the environmental impact of the compression work in presence of both gas-turbine driven and electric compressors. The operational problem includes the gas transport dynamic equations, detailed modeling of compressor stations and control valves, while handling complex branch and looped networks with possible reverse flow. To address large-scale problems, a graph reduction procedure and a novel bilevel decomposition algorithm are developed. This methodology, validated with real data, enables the optimization of the nationwide Italian network, comprising 51 compressors and 9727 km of pipes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"195 ","pages":"Article 109006"},"PeriodicalIF":3.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348535","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 : 2025-01-13DOI: 10.1016/j.compchemeng.2025.109003
Venkata Reddy Palleti
Water Distribution Networks (WDNs) are one of the most important critical infrastructures in any nation. WDNs are often prone to either accidental or intentional contamination. Intentional contamination, like terrorist attacks on WDNs, can lead to poisoned water, causing many fatalities and large economic consequences. In order to protect against these attacks, an efficient sensor network design is required by placing a limited number of sensors in the network. In this work, we will design sensor networks to satisfy two criteria, namely, observability (ability to detect the contamination) and identifiability ability to detect and identify the contamination source). Hydraulic simulations are performed on a WDN subjected to variable demand conditions. We will map the problem of the sensor network to a minimum set cover problem. A greedy heuristic algorithm is used to obtain the sensor network design under variable demand conditions. The proposed methodology is illustrated on a real life WDN.
{"title":"Optimal sensor placement for contamination detection and identification in water distribution networks under demand uncertainty","authors":"Venkata Reddy Palleti","doi":"10.1016/j.compchemeng.2025.109003","DOIUrl":"10.1016/j.compchemeng.2025.109003","url":null,"abstract":"<div><div>Water Distribution Networks (WDNs) are one of the most important critical infrastructures in any nation. WDNs are often prone to either accidental or intentional contamination. Intentional contamination, like terrorist attacks on WDNs, can lead to poisoned water, causing many fatalities and large economic consequences. In order to protect against these attacks, an efficient sensor network design is required by placing a limited number of sensors in the network. In this work, we will design sensor networks to satisfy two criteria, namely, observability (ability to detect the contamination) and identifiability ability to detect and identify the contamination source). Hydraulic simulations are performed on a WDN subjected to variable demand conditions. We will map the problem of the sensor network to a minimum set cover problem. A greedy heuristic algorithm is used to obtain the sensor network design under variable demand conditions. The proposed methodology is illustrated on a real life WDN.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 109003"},"PeriodicalIF":3.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136737","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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