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Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-03-02 Luisa Peterson, Jens Bremer, Kai Sundmacher
In this study, we critically examine the performance of hybrid and purely data-driven models in reactor systems using catalytic methanation in a continuously stirred tank reactor as a representative case. Our comparative analysis includes four models: one purely data-driven model and three hybrid models. These hybrid models blend data-driven and mechanistic approaches, using data-driven submodels for
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Stable optimisation-based scenario generation via game theoretic approach Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-03-02 Georgios L. Bounitsis, Lazaros G. Papageorgiou, Vassilis M. Charitopoulos
Systematic scenario generation (SG) methods have emerged as an invaluable tool to handle uncertainty towards the efficient solution of stochastic programming (SP) problems. The quality of SG methods depends on their consistency to generate scenario sets which guarantee stability on solving SPs and lead to stochastic solutions of good quality. In this context, we delve into the optimisation-based Distribution
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Machine learning-based optimization of a multi-step ion exchange chromatography for ternary protein separation Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-25 Chaoying Ding, Marianthi Ierapetritou
Ion-exchange chromatography is an essential but complicated step in the biopharmaceutical downstream process, with multiple factors affecting the separation efficiency. Model-based optimization can help expedite process developments with limited time and resource investments. To address the mechanistic model's high computational complexity, a machine learning (ML)-based optimization framework was introduced
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Two-stage chance-constrained programming based on Gaussian mixture model and piecewise linear decision rule for refinery optimization Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-21 Yu Yang
The two-stage chance-constrained program (CCP) is studied for a refinery optimization problem. In stage-I, the refinery decision-makers determine the type and quantity of crude oil procurement under operational uncertainties to maximize the expected profit under all possibilities. In stage-II, process unit flowrates are adjusted based on the realized uncertainties and available crude oil, while introducing
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A two-layer optimization method for maintenance task scheduling considering multiple priorities Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-19 Xiaoyong Gao, Shaowei Luo, Diao Peng, Guofeng Kui, Yi Xie, Juan Wu, Jun Pan, Xin Zuo, Tao Chen
Timely and effective maintenance scheduling is the key to the safe and stable operation in oil and gas fields. Large-scale maintenance tasks with multiple priorities are difficult to complete in an acceptable time. To accelerate computation, a two-layer strategy is proposed. At the upper layer, a lumped general task for each well cluster is generated. This lumping allows the upper layer model to concentrate
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Optimal operation of a natural gas sweetening plant Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-18 Mahdi Mohajeri, Mehdi Panahi, Akbar Shahsavand
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Computer-aided molecular refrigerant design for adsorption chillers based on classical density functional theory and PC-SAFT Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-17 Fabian Mayer, Lukas Spiekermann, Lisa Rueben, Philipp Rehner, Jan Seiler, Johannes Schilling, Joachim Gross, André Bardow
Adsorption chillers are a promising technology for sustainable cooling. The performance of adsorption chillers is highly influenced by the selection of the refrigerant. Still, systematic selection of refrigerants is challenging because evaluating adsorption properties requires significant experimental or simulation efforts. Thus, refrigerant design options are often limited.
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Algebraic surrogate-based flexibility analysis of process units with complicating process constraints Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-16 Tim Forster, Daniel Vázquez, Isabela Fons Moreno-Palancas, Gonzalo Guillén-Gosálbez
Flexibility analyses are widespread in chemical engineering to quantify allowed deviations from nominal conditions. Standard approaches to perform flexibility analysis can be hard to apply if process constraints are difficult to handle, as it happens in bioprocesses with dynamic constraints. Here, focusing on the computation of the traditional flexibility index in problems with complicating constraints
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ALARM RATIONALIZATION AND DYNAMIC RISK ANALYSES FOR RARE ABNORMAL EVENTS Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-16 Vikram Sudarshan, Warren D. Seider, Amish J. Patel, Ulku G. Oktem, Jeffrey E. Arbogast
In this paper, improved alarm rationalization strategies are introduced to evaluate the quality of the multivariate alarm systems developed in previous work – with the alarm thresholds and response actions modified appropriately, based on key statistical metrics. For an exothermic CSTR, our strategies resulted in a significant reduction in the number of nuisance alarms, focusing on only quality alarms
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Game-theoretic optimisation of supply chain design with customer contracts: The case of industrial gases market Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-15 Asimina Marousi, Karthik Thyagarajan, Jose M. Pinto, Lazaros G. Papageorgiou, Vassilis M. Charitopoulos
Contemporary process industries are confronted with volatile market conditions that jeopardise their financial sustainability. While mature markets, e.g. industrial gases, transition to oligopoly structures, the supply chain operation should adapt to a more customer-centric focus. Key issues related to the modelling and impact of the related contractual agreements between firms and customers remain
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Utilizing modern computer architectures to solve mathematical optimization problems: A survey Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-15 David E. Bernal Neira, Carl D. Laird, Laurens R. Lueg, Stuart M. Harwood, Dimitar Trenev, Davide Venturelli
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Optimization of distillation configurations for multicomponent-product distillations Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-15 Tony Joseph Mathew, Sundar Narayanan, Amrit Jalan, Logan R. Matthews, Himanshu Gupta, Rustom Billimoria, Carla Sofia Pereira, Chris Goheen, Mohit Tawarmalani, Rakesh Agrawal
Several optimization formulations exist in the literature for optimizing distillation configurations for unicomponent-product distillations (UPD), i.e., distillations where each product consists of only a single component. However, many separations either desire product streams composed of multiple components based on their end-use property requirements or tolerate such multicomponent products in exchange
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A virtual screening framework based on the binding site selectivity for small molecule drug discovery Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-13 Xinhao Che, Qilei Liu, Fang Yu, Lei Zhang, Rafiqul Gani
Structure-based virtual screening of binding of candidate drug molecules is a topic of increasing interest in the discovery of small molecule drugs. As the same drug molecule may bind to different binding sites on a target protein, the binding site selectivity that is related to the binding tendency of candidate drug molecules to different binding sites after reaching the target protein need to be
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Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-07 Kumail Alhamoud, Yasir Ghunaim, Abdulelah S. Alshehri, Guohao Li, Bernard Ghanem, Fengqi You
Predicting stable 3D molecular conformations from 2D molecular graphs is a challenging and resource-intensive task, yet it is critical for various applications, particularly drug design. Density functional theory (DFT) calculations set the standard for molecular conformation generation, yet they are computationally intensive. Deep learning offers more computationally efficient approaches, but struggles
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Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-07 Alireza Valizadeh, Mohammad Hossein Amirhosseini, Yousef Ghorbani
This paper presents an in-depth exploration of the application of machine learning techniques in battery recycling, aiming to enhance sustainability and efficiency in the process. The research objectives encompass three main aspects: (1) investigating the potential of using Machine Learning algorithms for predicting battery recycling potential, optimizing recycling processes, and improving resource
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Non-myopic Bayesian optimization using model-free reinforcement learning and its application to optimization in electrochemistry Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-07 Mujin Cheon, Haeun Byun, Jay H. Lee
Bayesian Optimization (BO) is a robust tool for tackling black-box optimization problems, yet traditional acquisition functions often suffer from a short-sighted approach, leading to suboptimal sampling. In this study, we introduce a novel methodology that defines a Markov Decision Process (MDP) tailored for BO, enabling the incorporation of Reinforcement Learning (RL) into the BO framework. Our RL-based
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Hierarchical heat transfer modeling of a continuous millireactor Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-07 Moritz J. Begall, Frank Herbstritt, Anne-Laura Sengen, Adel Mhamdi, Joachim Heck, Alexander Mitsos
Continuous millireactors are important but complex devices. Knowledge of their heat transfer characteristics is essential for their design and operation, but can be difficult to determine with experiments alone. We present a hierarchical CFD model which simulates the fluid flow and heat transfer of a Miprowa Lab millireactor in three increasing levels of detail, and which is validated with experimental
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A mixed integer linear programming approach for the design of chemical process families Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-05 Georgia Stinchfield, Joshua C. Morgan, Sakshi Naik, Lorenz T. Biegler, John C. Eslick, Clas Jacobson, David C. Miller, John D. Siirola, Miguel Zamarripa, Chen Zhang, Qi Zhang, Carl D. Laird
The need for rapid and widespread deployment of new technologies to address climate change goals (e.g., deep, economy-wide decarbonization) presents new opportunities for advancing modular design strategies. Conventional engineering approaches focus on unique designs for each installation, while missing opportunities for manufacturing standardization. Extending insights from the automotive industry
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Quality control in particle precipitation via robust optimization Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-03 Martina Kuchlbauer, Jana Dienstbier, Adeel Muneer, Hanna Hedges, Michael Stingl, Frauke Liers, Lukas Pflug
We propose a robust optimization approach to mitigate the impact of uncertainties in particle precipitation. Our model of particle synthesis incorporates, as partial differential equations, nonlinear and nonlocal population balance equations. The optimization goal is to design products with desired size distributions. Recognizing the impact of uncertainties, we extend the model to robustly hedge against
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Two-dimensional optimization design of constrained minmax model predictive tolerant-fault control for nonlinear batch processes Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-03 Limin Wang, Hui Li, Ridong Zhang, Furong Gao
The presence of actuator faults and disturbances, coupled with the nonlinear characteristics inherent in batch processes, poses challenges to the design of high-precision control algorithms, especially for variables that vary over a large range. To solve this issue, we propose a two-step 2D constrained model predictive fault-tolerant control method that integrates the theory of min-max optimization
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Energy-efficient heterogeneous triple-column azeotropic distillation process for recovery of ethyl acetate and methanol from wastewater Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-03 Ao Yang, Shirui Sun, Zong Yang Kong, Shuangshuang Zhu, Jaka Sunarso, Weifeng Shen
The challenging separation of ethyl acetate (EtAC) and methanol (MeOH) from wastewater involves two azeotropes and a distillation boundary. Although extractive distillation (ED) has been explored, the heterogeneous region within the ternary azeotropic mixture has been overlooked, which suggests the possibility of using a decanter to cross the distillation boundary when the feed composition is present
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Solving crystallization/precipitation population balance models in CADET, part I: Nucleation growth and growth rate dispersion in batch and continuous modes on nonuniform grids Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-28 Wendi Zhang, Todd Przybycien, Johannes Schmölder, Samuel Leweke, Eric von Lieres
We have developed, implemented and validated 1D and 2D population balance models (PBMs) in the open-source process simulator CADET. 1D PBMs incorporate the particle size as an internal coordinate and are associated with dynamic mass balances to describe particle-based processes in batch and continuous stirred tank reactors. 2D PBMs include the spatial position as an additional external coordinate to
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Computational Enhancements of Continuous Production Scheduling MILPs Using Tightening Constraints Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-02-01 Amin Samadi, Christos T. Maravelias
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Multi-technology separation system synthesis Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-29 Garry S.P. Taifan, Christos T. Maravelias
We address the synthesis of separation systems when multiple technologies are available. Since different technologies exploit different properties, we introduce a component ranking system based on those properties and use it to (1) construct matrices that identify possible separation splits and (2) generate a network-based superstructure that encompasses numerous promising configurations comprising
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A Multi-Bucket Time Representation Framework for Optimal Scheduling in Beverage Production Facilities Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-26 M.E. Samouilidou, G.P. Georgiadis, M.C. Georgiadis
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Improving profitability of continuous processes facing raw material variability through data-driven SMB-PLS model-based adaptive control Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-26 Adéline Paris, Carl Duchesne, Éric Poulin
Reducing the impact of lot-to-lot raw material variability through optimization of operating conditions is key when the lots are already purchased, and available in inventory. The objective of this paper is to provide a framework to optimize operating conditions to maximize profitability while aiming at achieving product quality targets each time a new lot of raw material is fed to a continuous process
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A simulation-based model studying monoethanolamine and aprotic heterocyclic anion ionic liquid (AHA-IL) mixtures for carbon capture Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-25 Adhish Chandra Saketh Madugula, Clayton Jeffryes, James Henry, John Gossage, Tracy J. Benson
Capturing of waste CO2, particularly from large industrial point sources, is necessary for either permanent storage or CO2 utilization. Amine solvents used for carbon capture have certain drawbacks, particularly the high energy required by the reboiler for solvent regeneration and degradation of the amine with absorption/regeneration cycling. These challenges may be overcome using ionic liquids (ILs)
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Long-term planning and coupling optimization of multi-regional natural gas and hydrogen supply systems: A case study of China Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-22 Jiaqi Zhang, Zheng Li, Xiaoying Zheng, Pei Liu
Low-carbon transition of energy systems features more natural gas and hydrogen consumption to replace coal and oil. Planning of natural gas and hydrogen supply systems with multiple supply sources, end-consumers, large infrastructures and spatio-temporal mismatches are challenging tasks. In this study, two long-term, multi-regional and monthly time-scale optimization models for natural gas and hydrogen
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Multi-objective optimization of food-energy-water nexus via crops land allocation Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-22 Anubha Agrawal, Bhavik R Bakshi, Hariprasad Kodamana, Manojkumar Ramteke
Bioenergy, specifically produced from crop residue is land and water extensive and should not interfere with food nutrition, water supply, and land use changes. We propose a novel strategy hinging on optimal land allocation in the food-energy-water nexus for various crops to maximize the food and bioenergy production values while minimizing water consumption and satisfying nutritional and dietary constraints
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Soft-sensor estimation via parameter fitting and dynamic optimization in an experimental batch butadiene homopolymerization reactor Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-16 Antonio Flores-Tlacuahuac, Enrique Saldívar-Guerra, Ramón Díaz de León, Ricardo López-González, Luis Antonio Rodriguez-Guadarrama
In this work, we address modeling and experimental tasks to develop an industrial process for the anionic polymerization of butadiene, which is a key compound used in the production of high-performance tires. The aim of this study was to determine the processing conditions that lead to the efficient manufacturing of polybutadiene. Accordingly, an experimental polymer reaction facility was constructed
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Development of a multi-leader multi-follower game to design industrial symbioses Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-20 Manuel A. Ramos, Marianne Boix, Didier Aussel, Ludovic Montastruc
The implementation of industrial symbioses all over the world has been proven to be a significant lever to decarbonize production facilities. By enhancing cooperation and exchanging resources, important decrease of emissions can be achieved while reducing operating costs. In the light of our previous work, a game theory model is developed to design resources exchanges in eco-industrial parks (EIP)
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Piecewise linear approximation for MILP leveraging piecewise convexity to improve performance Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-19 Felix Birkelbach, David Huber, René Hofmann
To realize adaptive operation planning with MILP unit commitment, piecewise-linear approximations of the functions that describe the operating behavior of devices in the energy system have to be computed. We present an algorithm to compute a piecewise-linear approximation of a multi-variate non-linear function. The algorithm splits the domain into two regions and approximates each region with a set
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Process expert knowledge is essential in creating value from data-driven industrial soft sensors Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-19 Tim Offermans, Ewa Szymańska, Francisco A.A. Souza, Jeroen J. Jansen
The objective of Industry 5.0 is to (re)centre the human operator amidst digital process automation. This requires new data processing technologies that extract human expertise and integrate it with advanced data modelling techniques to enhance human-computer collaboration. In this work, we present an integrated and systematic approach that combines contemporary data modelling technology with process
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Orthogonal projection based statistical feature extraction for continuous process monitoring Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-18 Cheng Ji, Fangyuan Ma, Jingde Wang, Wei Sun
Multivariate statistical techniques have been widely applied in industrial processes to detect abnormal behaviors, while their performance could be unsatisfactory due to insufficient extraction of complex data characteristics. A method named Orthogonal transformed statistics Mahalanobis distance (OTSMD) is developed to handle this issue. As a feature-based method, OTSMD simultaneously considers various
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Model-based safe reinforcement learning for nonlinear systems under uncertainty with constraints tightening approach Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-18 Yeonsoo Kim, Tae Hoon Oh
In chemical processes, the safety constraints must be satisfied despite any uncertainties. Reinforcement learning is an algorithm that learns optimal control policies through interaction with the system. Recently, studies have shown that well-trained controllers can improve the performance of chemical processes, but the actual application requires additional schemes to satisfy the constraints. In our
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Multivariable robust tube-based nonlinear model predictive control of mammalian cell cultures Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-18 L. Dewasme, M. Mäkinen, V. Chotteau
In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor
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Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-18 Tianqi Cui, Tom Bertalan, Nelson Ndahiro, Pratik Khare, Michael Betenbaugh, Costas Maranas, Ioannis G. Kevrekidis
Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies Here, we propose a physically-informed data-driven hybrid model (a “gray
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Waste-to-energy technology selection: A multi-criteria optimisation approach Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-17 Ahmed AlNouss, Mohammad Alherbawi, Prakash Parthasarathy, Naela Al-Thani, Gordon McKay, Tareq Al-Ansari
Waste is the most abundant biomass worldwide for renewable energy and value-added products generation. While technologies for the treatment of multiple waste categories continue to evolve, frameworks that facilitate strategic decision-making within bio-economies are required. Therefore, the aim of this research is to develop a framework that can identify optimal processing route for converting different
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A two-level MPC method for the operation of a gas pipeline system under demand variation Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-15 Yaran Bu, Christopher L.E. Swartz, Changchun Wu
A gas pipeline system is in an almost continual state of transition due to changing gas demand. A control method is required to keep a gas pipeline operating in an energy-saving status and provide reliable transmission service for the customers. Model predictive control (MPC) predicts the future states of a system, making it suitable for following gas demand changes. However, to avoid computation time
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Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-11 Thanh Tung Khuat, Robert Bassett, Ellen Otte, Alistair Grevis-James, Bogdan Gabrys
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead
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Design strategy research for synthesis of ethyl propionate and n-propyl propionate by reactive distillation Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-14 Yang Liu, Qingyue Zhao, Hongxing Wang
In this work, a design strategy for synthesis of n-propyl propionate and ethyl propionate by reactive distillation was proposed. Combined with previous work, the content of this research is mainly divided into two parts. Firstly, conceptual design of reactive distillation for synthesis of ethyl propionate was carried out by reactive residue curve maps analysis. Secondly, the reactive distillation process
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Exploring nontraditional LSTM architectures for modeling demethanizer column operations Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-12 Marta Mandis, Roberto Baratti, Jorge Chebeir, Stefania Tronci, José A. Romagnoli
Digital twins have recently attracted attention as a new technology that can facilitate the digital transformation of process industries. It may provide live, or near real-time, information and insights into the process and may be used for monitoring, control and optimization purposes. In this study, a digital twin has been developed for modelling the demethanizer column of a NGL separation plant.
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A reinforcement learning-based temperature control of fluidized bed reactor in gas-phase polyethylene process Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-11 Xiaodong Hong, Zhoupeng Shou, Wanke Chen, Zuwei Liao, Jingyuan Sun, Yao Yang, Jingdai Wang, Yongrong Yang
This study investigates using deep reinforcement learning (DRL) with proportional-integral-derivative (PID) control for temperature cascade control in a fluidized bed reactor within a commercial gas-phase polyethylene process. The heat exchange system's nonlinearity and frequent disturbances pose challenges for PID controllers, particularly under varying conditions. To address this, a PID-DRL cascade
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A hierarchical granger causality analysis framework based on information of redundancy for root cause diagnosis of process disturbances Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-10 Jian-Guo Wang, Rui Chen, Xiang-Yun Ye, Zhong-Tao Xie, Yuan Yao, Li-Lan Liu
The Granger causality (GC) test is a widely utilized method for diagnosing process disturbances’ root cause. However, its effectiveness is limited due to the challenge of handling variable redundancy, leading to potentially inaccurate results. To tackle this problem, this paper introduces a new redundancy detection technique incorporated into the GC test framework. The method introduces a sum-of-redundancy
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Hybrid model development and nonlinear model predictive control implementation for continuous dry granulation process Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-10 Yan-Shu Huang, Rexonni B. Lagare, Phoebe Bailey, David Sixon, Marcial Gonzalez, Zoltan K. Nagy, Gintaras V. Reklaitis
This study focuses on the development of a hybrid model to integrate roll compaction and ribbon milling operations to design and control a continuous dry granulation process. The proposed hybrid model has three features: (1) it compensates for underestimated roll gap measurements with knurled rolls, (2) it represents the bimodal size distribution of granules using five fitting parameters of the bimodal
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Surrogate-based optimisation of process systems to recover resources from wastewater Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-09 Alex Durkin, Lennart Otte, Miao Guo
Wastewater systems are transitioning towards integrative process systems to recover multiple resources whilst simultaneously satisfying regulations on final effluent quality. This work contributes to the literature by bringing a systems-thinking approach to resource recovery from wastewater, harnessing surrogate modelling and mathematical optimisation techniques to highlight holistic process systems
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Two-dimensional model-free Q-learning-based output feedback fault-tolerant control for batch processes Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-05 Huiyuan Shi, Wei Gao, Xueying Jiang, Chengli Su, Ping Li
For batch processes with partial actuator failures and unknown system dynamics, an innovative two-dimensional (2D) model-free Q-learning algorithm is proposed to obtain the optimal controller's gains, achieving output feedback fault-tolerant control. First, a 2D linear model is constructed to describe batch processes with partial actuator failures. Then, the state increments in the batch direction
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Robust-to-occlusion machine vision model for predicting quality variables with slow-rate measurements Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-05 Yousef Salehi, Ranjith Chiplunkar, Biao Huang
Efficient control and optimization of processes require fast-rate measurements of process variables. However, certain variables can only be measured at a slow rate due to technical or economic limitations. Video cameras are commonly available in process industry. Taking images regularly may provide valuable insights into the process dynamics. In this paper, a vision model is proposed to provide a fast-rate
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Multi-actor integrated modeling approaches in the context of Water-Energy-Food Nexus systems: Review Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-03 Amaya Saint Bois, Marianne Boix, Ludovic Montastruc
Water, energy and food are essential resources for human life; however, their security is increasingly threatened worldwide by climate change, urbanization, and population growth. The Nexus approach, conceptualized in 2011 by the Bonn Nexus Conference, addresses the interconnections and trade-offs of water-energy-food systems and analyzes the interdisciplinary and transdisciplinary dimensions of these
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A method to bridge energy and process system optimization: Identifying the feasible operating space for a methanation process in power-to-gas energy systems Comput. Chem. Eng. (IF 4.3) Pub Date : 2024-01-02 Yifan Wang, Luka Bornemann, Christiane Reinert, Niklas von der Assen
The growing adoption of renewable energy is driving the integration of new, complex process technologies into energy systems, presenting operational optimization challenges. Simple models are necessary for computational tractability, yet detailed models are essential for feasible operating decisions. This work introduces a method for optimally operating an energy system with integrated complex process
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Optimal design and robust operational management of regional bioethanol supply chain with various technological choices and uncertainty fusions Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-27 Xianling Huang, Ling Ji, Jianguang Yin, Guohe Huang
Bioethanol has emerged as a promising alternative to fossil fuels, but its commercialization is hindered by high costs and uncertainties surrounding feedstock supply and policies. To address these challenges, a two-stage stochastic robust programming model is developed for regional biorefineries planning and supply chain management with various technological choices and uncertainty fusions. The model
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Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-30 Malte Esders, Gimmy Alex Fernandez Ramirez, Michael Gastegger, Satya Swarup Samal
Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a flowsheet-like directed graph. We find that for smaller plants, this approach works
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Time-slice dynamic prediction and multiway serial PCA for batch industrial process monitoring Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-30 Hanqi Li, Mingxing Jia, Zhizhong Mao
This paper introduces an innovative method for batch industrial process monitoring and fault detection. By combining time-slice dynamic prediction with an enhanced adaptive multiway principal component analysis (PCA) technique, it aims to balance the trade-off between high fault detection rates and low false alarm rates in monitoring batch industrial data. The method extracts dynamic features from
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Integration of chemical process operation with energy, global market, and plant systems infrastructure Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-29 Jesus Flores-Cerrillo, Christopher L.E. Swartz, Ankur Kumar, Daniela Dering
Increased globalization, deregulation of energy markets, and environmental constraints, together with associated uncertainty, have created a highly dynamic and uncertain process manufacturing environment. Responding effectively to this increased variation and uncertainty is critical for a company to remain competitive. In this paper, we consider the plant infrastructure in relation to the energy and
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Levelized-cost optimal design of long-distance CO2 transportation facilities Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-24 Mohamed Mazhar Laljee, Farzad Hourfar, Yuri Leonenko, Ladan Khoshnevisan, Hedia Fgaier, Ali Almansoori, Ali Elkamel
With the need to develop carbon-neutral technologies and reduce greenhouse gas emissions to avert climate change, carbon capture, storage and utilization (CCS/CCUS) techniques have emerged as effective pathways to achieve these targets. Despite being a critical aspect of CCS/CCUS chains, carbon dioxide transport is an astonishingly under-researched domain. This study presents an efficient and robust
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Trends and perspectives in computed-aided process operations and control Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-28 Rahul Bindlish, Michael Baldea, Iiro Harjunkoski, Victor Zavala
Abstract not available
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Efficient computational strategies for a mathematical programming model for multi-echelon inventory optimization based on the guaranteed-service approach Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-24 V.G. Achkar, B.B. Brunaud, Rami Musa, I.E. Grossmann
This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly
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Identifying Key Drivers for a National Transition to Low Carbon Energy using Agent-based Supply Chain Models Comput. Chem. Eng. (IF 4.3) Pub Date : 2023-12-23 Vaiyaicheri S. Venkataramanan, Mohd Shahrukh, Dimitri J. Papageorgiou, Srinivasan Rajagopalan, Rajagopalan Srinivasan
Understanding robust pathways to achieve affordable, reliable, and equitable energy transitions to a decarbonized society has received growing attention. While existing energy transition literature emphasizes the role of government policy and technological innovation, little has been written to integrate “top-down” approaches encompassing government policy and “bottom-up” individual enterprise-level