Computers & Chemical Engineering最新文献

英文中文

Data-driven controller parameters online tuning method based on model-inherited trust region Bayesian optimization

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-08 DOI: 10.1016/j.compchemeng.2025.109141

Yuchao Qiu , Zuhua Xu , Jun Zhao , Chunyue Song , Xiaoping Zhu

Control performance degradation is a common phenomenon in chemical processes. How to achieve online tuning of controller parameters with few experiments while ensuring the stability and safety of the control system is a significant challenge. In this study, a data-driven controller parameters online tuning method based on model-inherited trust region Bayesian optimization is proposed. First, the objective function and safety constraints of Bayesian optimization for controller online tuning problem are excavated using control performance assessment (CPA) based on time series modeling. Second, utilizing the correlation between local modeling tasks, a model-inherited Gaussian process regression (GPR) method is proposed to build the accurate surrogate model between controller parameters and control performance. This surrogate model consists of two parts: one is the inheritance part of historical GPR models and the other is the residual part interpreted as a zero-mean Gaussian process, so that the model accuracy can be guaranteed with a small amount of evaluation data. Third, a constraint acquisition function based on expected improvement is designed to ensure safe exploration during the tuning procedure, in which feasibility probability of constraints such as overshoot and settling time from CPA are incorporated through a weighted approach. Moreover, a shape-adaptation update method of the trust region is developed to improve optimization efficiency and robustness. Finally, the effectiveness of the method is verified through two industrial cases.

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引用次数: 0

An adversarial twin-agent inverse proximal policy optimization guided by model predictive control

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-08 DOI: 10.1016/j.compchemeng.2025.109124

Nikita Gupta , Harikumar Kandath , Hariprasad Kodamana

Reward design is a key challenge in reinforcement learning (RL) as it directly affects the effectiveness of learned policies. Inverse Reinforcement Learning (IRL) attempts to solve this problem by learning reward functions from expert trajectories. This study utilizes a reward design using Adversarial IRL (AIRL) frameworks using expert trajectories from Model Predictive Control (MPC). On the contrary, there are also instances where a pre-defined reward function works well, indicating a potential trade-off between these two. To achieve this, we propose a twin-agent reinforcement learning framework where the first agent utilizes a pre-defined reward function, while the second agent learns reward in the AIRL setting guided by MPC with Proximal Policy Optimization (PPO) as the backbone (PPO-MPC-AIRL). The performance of the proposed algorithm has been tested using a case study, namely, mAb production in the bioreactor. The simulation results indicate that the proposed algorithm is able to reduce the root mean square error (RMSE) of set-point tracking by 18.38 % compared to the nominal PPO.

{"title":"An adversarial twin-agent inverse proximal policy optimization guided by model predictive control","authors":"Nikita Gupta , Harikumar Kandath , Hariprasad Kodamana","doi":"10.1016/j.compchemeng.2025.109124","DOIUrl":"10.1016/j.compchemeng.2025.109124","url":null,"abstract":"<div><div>Reward design is a key challenge in reinforcement learning (RL) as it directly affects the effectiveness of learned policies. Inverse Reinforcement Learning (IRL) attempts to solve this problem by learning reward functions from expert trajectories. This study utilizes a reward design using Adversarial IRL (AIRL) frameworks using expert trajectories from Model Predictive Control (MPC). On the contrary, there are also instances where a pre-defined reward function works well, indicating a potential trade-off between these two. To achieve this, we propose a twin-agent reinforcement learning framework where the first agent utilizes a pre-defined reward function, while the second agent learns reward in the AIRL setting guided by MPC with Proximal Policy Optimization (PPO) as the backbone (PPO-MPC-AIRL). The performance of the proposed algorithm has been tested using a case study, namely, mAb production in the bioreactor. The simulation results indicate that the proposed algorithm is able to reduce the root mean square error (RMSE) of set-point tracking by 18.38 % compared to the nominal PPO.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109124"},"PeriodicalIF":3.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852345","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}

引用次数: 0

The SABYDOMA Safety by Process Control framework for the production of functional, safe and sustainable nanomaterials

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-06 DOI: 10.1016/j.compchemeng.2025.109113

Argyri Kardamaki , Athanassios Nikolakopoulos , Mihalis Kavousanakis , Philip Doganis , Matt Jellicoe , William Stokes , Vesa Hongisto , Matthew Simmons , Thomas W. Chamberlain , Nikil Kapur , Roland Grafström , Andrew Nelson , Haralambos Sarimveis

The production of nanomaterials (NMs) has gained significant attention due to their unique properties and versatile applications in fields such as medicine, energy, and electronics. However, ensuring the large-scale synthesis of safe and sustainable NMs while maintaining their functionality remains a critical challenge. This study introduces the Safety by Process Control (SbPC) framework, a novel methodology integrating dynamic first-principles modeling, Model Predictive Control (MPC), and real-time safety monitoring. The framework employs a physics-based population balance model with a Method Of Moments (MOM) approximation to predict the evolution of key NM properties. A toxicity inferential sensor, built on experimental data, is integrated to facilitate real-time hazard assessment. The efficiency of the proposed framework is demonstrated using a continuous silver nanoparticle (Ag NP) production system as a case study. The proposed approach ensures the production of high-quality, safe, and sustainable NMs, aligning with Safe and Sustainable by Design (SSbD) principles and addressing gaps in current NM manufacturing processes. The framework’s adaptability to other NM types highlights its potential as a transformative tool for sustainable nanotechnology.

{"title":"The SABYDOMA Safety by Process Control framework for the production of functional, safe and sustainable nanomaterials","authors":"Argyri Kardamaki , Athanassios Nikolakopoulos , Mihalis Kavousanakis , Philip Doganis , Matt Jellicoe , William Stokes , Vesa Hongisto , Matthew Simmons , Thomas W. Chamberlain , Nikil Kapur , Roland Grafström , Andrew Nelson , Haralambos Sarimveis","doi":"10.1016/j.compchemeng.2025.109113","DOIUrl":"10.1016/j.compchemeng.2025.109113","url":null,"abstract":"<div><div>The production of nanomaterials (NMs) has gained significant attention due to their unique properties and versatile applications in fields such as medicine, energy, and electronics. However, ensuring the large-scale synthesis of safe and sustainable NMs while maintaining their functionality remains a critical challenge. This study introduces the Safety by Process Control (SbPC) framework, a novel methodology integrating dynamic first-principles modeling, Model Predictive Control (MPC), and real-time safety monitoring. The framework employs a physics-based population balance model with a Method Of Moments (MOM) approximation to predict the evolution of key NM properties. A toxicity inferential sensor, built on experimental data, is integrated to facilitate real-time hazard assessment. The efficiency of the proposed framework is demonstrated using a continuous silver nanoparticle (Ag NP) production system as a case study. The proposed approach ensures the production of high-quality, safe, and sustainable NMs, aligning with Safe and Sustainable by Design (SSbD) principles and addressing gaps in current NM manufacturing processes. The framework’s adaptability to other NM types highlights its potential as a transformative tool for sustainable nanotechnology.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109113"},"PeriodicalIF":3.9,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838879","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}

引用次数: 0

A robust deep reinforcement learning approach for the control of crystallization processes

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-04 DOI: 10.1016/j.compchemeng.2025.109114

José Rodrigues Torraca Neto , Bruno Didier Olivier Capron , Argimiro Resende Secchi

This work investigates the application of reinforcement learning (RL) for crystallization process control, focusing on robustness against parametric uncertainty and measurement noise. A curriculum learning approach with progressive uncertainty scaling and soft constraint enforcement was developed to enhance RL agent adaptability and performance. Four actor–critic RL algorithms—Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor–Critic (SAC), and Proximal Policy Optimization (PPO)—were trained and evaluated using baseline, domain randomization, and curriculum learning strategies. The performance of each algorithm was assessed based on key control metrics, including setpoint tracking, control smoothness, and constraint satisfaction, with Nonlinear Model Predictive Control (NMPC) serving as an oracle benchmark. The results show that PPO consistently outperformed other algorithms, achieving the lowest mean absolute percentage error (MAPE) for critical process parameters (2.20%) and the lowest violation probability (0.67%) under curriculum learning. This strategy also reduced control variability, with PPO achieving a control variability index (CVI) of 0.008, indicating smooth control actions. While DDPG and TD3 exhibited competitive performance, SAC suffered from high fluctuations and the lowest rewards across all training strategies, highlighting its limitations in stability-critical applications. The findings highlight the effectiveness of curriculum learning with soft constraints in enhancing RL performance for industrial process control, establishing PPO as a reliable solution for robust crystallization control.

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引用次数: 0

Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-04 DOI: 10.1016/j.compchemeng.2025.109119

Peter Jul-Rasmussen , Monesh Kumar , Jóse Pinto , Rui Oliveira , Xiaodong Liang , Jakob Kjøbsted Huusom

With increased data availability in the (bio)chemical processing industries, there is a renewed interest in leveraging data-based methods to improve process operations. While data-based approaches enable the modeling of phenomena that are difficult to model mechanistically, they also have drawbacks, especially when presented with serially correlated process data with low variation typically found in the process industries. The limitations in both mechanistic and data-based modeling can be addressed through hybrid approaches. The combination of mechanistic and data-based models into hybrid semi-parametric models has shown great promise in mitigating such limitations over the last 30 years. More recently, physics-informed learning approaches have been proposed as an alternative method for embedding process knowledge in data-based models. This work provides a comparative study of hybrid semi-parametric modeling and Physics-Informed Recurrent Neural Networks (PIRNNs) applied to a pilot-scale bubble column aeration case study. The developed models are compared based on the ease of training, the models’ adherence to the governing system equations, the prediction accuracy when reducing the measurement frequency, and the model performance when reducing the quantity of training data. For the considered case study, the hybrid semi-parametric modeling approach generally resulted in superior model performance with high prediction accuracy, good adherence to the physics, and good performance when reducing the quantity of training data.

{"title":"Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks","authors":"Peter Jul-Rasmussen , Monesh Kumar , Jóse Pinto , Rui Oliveira , Xiaodong Liang , Jakob Kjøbsted Huusom","doi":"10.1016/j.compchemeng.2025.109119","DOIUrl":"10.1016/j.compchemeng.2025.109119","url":null,"abstract":"<div><div>With increased data availability in the (bio)chemical processing industries, there is a renewed interest in leveraging data-based methods to improve process operations. While data-based approaches enable the modeling of phenomena that are difficult to model mechanistically, they also have drawbacks, especially when presented with serially correlated process data with low variation typically found in the process industries. The limitations in both mechanistic and data-based modeling can be addressed through hybrid approaches. The combination of mechanistic and data-based models into hybrid semi-parametric models has shown great promise in mitigating such limitations over the last 30 years. More recently, physics-informed learning approaches have been proposed as an alternative method for embedding process knowledge in data-based models. This work provides a comparative study of hybrid semi-parametric modeling and Physics-Informed Recurrent Neural Networks (PIRNNs) applied to a pilot-scale bubble column aeration case study. The developed models are compared based on the ease of training, the models’ adherence to the governing system equations, the prediction accuracy when reducing the measurement frequency, and the model performance when reducing the quantity of training data. For the considered case study, the hybrid semi-parametric modeling approach generally resulted in superior model performance with high prediction accuracy, good adherence to the physics, and good performance when reducing the quantity of training data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109119"},"PeriodicalIF":3.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808229","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}

引用次数: 0

Design of plastic waste chemical recycling process considering uncertainty

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-03 DOI: 10.1016/j.compchemeng.2025.109128

Zhifei Yuliu, Yuqing Luo, Marianthi G. Ierapetritou

The selection of mixed plastic waste recycling technologies directly influences product distribution and thus the overall process economics. This study presents an integrated chemical recycling process that combines pyrolysis, hydrogenolysis, and hydrocracking to transform plastic waste directly into high-value fuel products, eliminating the need for further upgrading. A mixed-integer nonlinear programming model is formulated to consider the selection of reaction pathways considering fuel properties to ensure compliance with product specifications. Robust optimization is used to incorporate the effect of fuel property and economic uncertainty on the process design. Under nominal conditions, the optimized chemical recycling process demonstrates strong economic performance with a unit net present value of $366/t plastic waste. Different levels of conservatism are considered to account for uncertainty. The optimized process shows a robust performance by leveraging different combinations of depolymerization technologies and adjusting the product portfolio.

引用次数: 0

Design-of-experiments based modeling & optimization of LGA cooling crystallization via continuous oscillatory baffled crystallizer

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-04-02 DOI: 10.1016/j.compchemeng.2025.109126

Mingyan Zhao , Tao Liu , Bo Song , Ji Fan , Xiongwei Ni , Rolf Findeisen

A novel data-driven modeling and optimization method is proposed in this paper for cooling crystallization of l-glutamic acid (LGA) via a continuous oscillatory baffled crystallizer (COBC), based on the design of experiments (DoEs) for the main operating conditions of zone temperature setting and volume net flowrate. The crystal size distribution (CSD) can be effectively predicted by constructing a data-mapping model with double-layer basis functions, where the first layer is composed of wavelet basis functions for reshaping the steady-state CSD in each operating zone of COBC, and the second layer consists of polynomial basis functions for reflecting the nonlinear relationship between the above operating conditions and the corresponding CSD in each zone. Furthermore, a comprehensive cost function related to the desired crystal size, the distribution variance of product crystals and throughput is introduced to design an optimization method for the above operating conditions. A guaranteed convergence particle swarm optimization (GCPSO) algorithm is offered to solve the nonconvex optimization problem based on the established CSD prediction model. Experimental results on the continuous crystallization of LGA demonstrate that the above cost function and the desired crystal product yield can be improved over 23 % and 9 %, respectively, in comparison with all tests by DoEs.

{"title":"Design-of-experiments based modeling & optimization of LGA cooling crystallization via continuous oscillatory baffled crystallizer","authors":"Mingyan Zhao , Tao Liu , Bo Song , Ji Fan , Xiongwei Ni , Rolf Findeisen","doi":"10.1016/j.compchemeng.2025.109126","DOIUrl":"10.1016/j.compchemeng.2025.109126","url":null,"abstract":"<div><div>A novel data-driven modeling and optimization method is proposed in this paper for cooling crystallization of <span>l</span>-glutamic acid (LGA) via a continuous oscillatory baffled crystallizer (COBC), based on the design of experiments (DoEs) for the main operating conditions of zone temperature setting and volume net flowrate. The crystal size distribution (CSD) can be effectively predicted by constructing a data-mapping model with double-layer basis functions, where the first layer is composed of wavelet basis functions for reshaping the steady-state CSD in each operating zone of COBC, and the second layer consists of polynomial basis functions for reflecting the nonlinear relationship between the above operating conditions and the corresponding CSD in each zone. Furthermore, a comprehensive cost function related to the desired crystal size, the distribution variance of product crystals and throughput is introduced to design an optimization method for the above operating conditions. A guaranteed convergence particle swarm optimization (GCPSO) algorithm is offered to solve the nonconvex optimization problem based on the established CSD prediction model. Experimental results on the continuous crystallization of LGA demonstrate that the above cost function and the desired crystal product yield can be improved over 23 % and 9 %, respectively, in comparison with all tests by DoEs.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109126"},"PeriodicalIF":3.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815406","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}

引用次数: 0

Enhancing data quality in wastewater processes: Missing data imputation with deep Variational Autoencoders and genetic algorithms 提高废水处理过程中的数据质量：利用深度变异自动编码器和遗传算法估算缺失数据

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-03-31 DOI: 10.1016/j.compchemeng.2025.109123

Christian Kazadi Mbamba , Philip Keymer , Maira Alvi , Sebastian O.N. Topalian , Fareed Ud Din , Damien J. Batstone

Missing data is a persistent challenge in wastewater analysis, often leading to biased results and reduced accuracy. This study introduces an innovative Automated Machine Learning (AutoML) framework that combines deep learning-based variational autoencoders (VAEs) and genetic algorithms (GAs) to address this issue. VAEs are employed to impute missing values by learning latent data representations, while GAs optimize the VAE architecture and hyperparameters, including the size of the latent space. The framework is specifically designed to handle the complex and nonlinear relationships in wastewater datasets.

The framework was trained and validated using data from a full-scale water resource recovery facility. The imputed data from the optimized VAE, developed using the GA-based AutoML framework, is then used to train predictive models. Experimental evaluations demonstrate the effectiveness of the proposed approach over traditional imputation methods. The results reveal that the models can accurately predict key variables such as ammonia nitrogen (NH₄-N), nitrate nitrogen (NO₃-N), pH, and biogas flow rate, using imputed data. The scalability and adaptability of this framework make it valuable for real-time wastewater monitoring and predictive analytics.

{"title":"Enhancing data quality in wastewater processes: Missing data imputation with deep Variational Autoencoders and genetic algorithms","authors":"Christian Kazadi Mbamba , Philip Keymer , Maira Alvi , Sebastian O.N. Topalian , Fareed Ud Din , Damien J. Batstone","doi":"10.1016/j.compchemeng.2025.109123","DOIUrl":"10.1016/j.compchemeng.2025.109123","url":null,"abstract":"<div><div>Missing data is a persistent challenge in wastewater analysis, often leading to biased results and reduced accuracy. This study introduces an innovative Automated Machine Learning (AutoML) framework that combines deep learning-based variational autoencoders (VAEs) and genetic algorithms (GAs) to address this issue. VAEs are employed to impute missing values by learning latent data representations, while GAs optimize the VAE architecture and hyperparameters, including the size of the latent space. The framework is specifically designed to handle the complex and nonlinear relationships in wastewater datasets.</div><div>The framework was trained and validated using data from a full-scale water resource recovery facility. The imputed data from the optimized VAE, developed using the GA-based AutoML framework, is then used to train predictive models. Experimental evaluations demonstrate the effectiveness of the proposed approach over traditional imputation methods. The results reveal that the models can accurately predict key variables such as ammonia nitrogen (NH<sub>4</sub>-N), nitrate nitrogen (NO<sub>3</sub>-N), pH, and biogas flow rate, using imputed data. The scalability and adaptability of this framework make it valuable for real-time wastewater monitoring and predictive analytics.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109123"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800426","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}

引用次数: 0

A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-03-30 DOI: 10.1016/j.compchemeng.2025.109107

Jiaorao Wang , Lishuai Li , S. Joe Qin

Complex chemical plant operations exhibit multi-scale dynamics and multi-mode characteristics. Existing methods typically address either multi-scale, dynamic, or multi-mode monitoring separately. This paper proposes a general hierarchical scheme for dynamic monitoring of systems with multi-mode dynamic behaviors. The core strength of the proposed method lies in its iterative procedure, which comprises two steps: dynamic pattern modeling and mode segmentation. Firstly, dynamic patterns across different modes are captured using latent vector autoregressive (LaVAR) modeling. In mode segmentation, data representing new dynamic patterns are filtered for the construction of the next LaVAR model, guided by two monitoring indices. The hierarchical structure sequentially extracts dynamic patterns, inherently dealing with unbalanced data common in industrial applications. Experiments are conducted to demonstrate the effectiveness of the proposed scheme for multi-mode dynamic system monitoring.

引用次数: 0

Integrated optimisation of biowaste-based green hydrogen supply chains from economic, environmental, and safety perspectives

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2025-03-29 DOI: 10.1016/j.compchemeng.2025.109120

Qi Hao Goh , Wen-Shan Tan , Yong Kuen Ho , Irene Mei Leng Chew

This study proposes an integrated optimisation approach for designing a biowaste-based green hydrogen supply chain (GHSC) using palm oil industrial wastes. For the first time, safety considerations are emphasised in the optimisation of biowaste-based GHSC design, alongside economic and environmental aspects. Additionally, the upstream supply chain superstructure has been revised to incorporate multi-centralised biowaste supply hubs to support streamlined waste management and resource allocation. A mixed-integer programming (MIP) model has been developed to address GHSC design, aiming to enhance profitability while reducing total carbon footprints and associated safety risks. Using a case study involving 34 palm oil mills in Malaysia, this study delves into the optimal design of a biowaste-based GHSC across various optimisation scenarios, including single- and multi-objective cases. Furthermore, the study investigates the potential of integrating technologies such as solar-powered electrolysis to augment green hydrogen supply, particularly when local biowaste resources are limited. The results demonstrate that the proposed framework effectively achieves integrated GHSC optimisation by identifying optimal resource and product distribution, as well as suitable production and storage technologies. Sensitivity analysis indicates that the integration of solar-powered electrolysis is economically feasible only if hydrogen prices exceed USD 5.36/kg H₂.

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