Article
Engineering, Chemical
Jianlin Yan, Jiyan Liu, Junyao Ren, Yang Wu, Xinnong Li, Tao Sun, Lanyi Sun
Summary: The recycling of wastewater in the chemical industry is important for recovering valuable solvents and preventing pollution. In this study, benzene/isopropanol/water mixtures were separated using reactive-extractive distillation column processes. Thermodynamic analysis and optimization with a genetic algorithm were conducted to determine suitable operating conditions. The proposed processes showed significant reductions in cost and CO2 emissions compared to traditional processes, making them more economically and environmentally sustainable.
SEPARATION AND PURIFICATION TECHNOLOGY
(2022)
Article
Automation & Control Systems
Yousef Abdi, Mohammad Asadpour, Yousef Seyfari
Summary: In this study, a hybrid micro multi-objective evolutionary algorithm called mu MOSM is proposed to effectively address diversity loss and accelerate the convergence rate in approximating Pareto front solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Chanjuan Liu, Shike Ge, Yuanke Zhang
Summary: Identifying critical nodes in a network is essential for understanding its characteristics and controlling its structure. The Cardinality-constrained CNP (CC-CNP) is a challenging combinatorial optimization problem that aims to minimize the number of deleted nodes while maintaining a desired level of connectivity in the residual subgraph. CC-CNP has applications in epidemic control, power network maintenance, and traffic network control.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Junbo Lian, Guohua Hui
Summary: This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), which is a metaheuristic algorithm inspired by human evolution. The algorithm divides the global search process into two distinct phases and uses unique search strategies. Comparative analysis with other algorithms demonstrates the effectiveness of HEOA in approximating optimal solutions for complex global optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Wenwu Han, Qianwang Deng, Guiliang Gong, Like Zhang, Qiang Luo
Summary: This study focuses on a new realistic hybrid flow shop scheduling problem with worker constraint (HFSSPW) and proposes seven multi-objective evolutionary algorithms to solve the problem, incorporating the earliest due date (EDD) rule into the heuristic decoding methods. The computational results demonstrate the excellent performance of the proposed algorithms in terms of makespan objective.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Xue Feng, Anqi Pan, Zhengyun Ren, Zhiping Fan
Summary: Balancing convergence and diversity is a challenge in multi-objective optimization problems, especially when the proportion of feasible regions is low. This paper proposes a constrained multi-objective optimization algorithm based on a hybrid driven strategy to enhance the feasibility and diversity performance of Pareto solutions. The algorithm outperforms peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.
INFORMATION SCIENCES
(2022)
Article
Engineering, Chemical
Qing Zhao, Yanan Li, Chen Li, Min Yan, Zhaoyou Zhu, Peizhe Cui, Jianguang Qi, Yinglong Wang, Chuanxing Wang
Summary: This study proposes efficient transesterification synthesis and separation processes for n-butyl acetate (nBuOAC) and methanol (MEOH), and the optimal process conditions were obtained through calculations and optimizations. The results show that the reactive distillation-pervaporation (RDPV) process performs best economically and environmentally.
SEPARATION AND PURIFICATION TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Xinning Li, Hu Wu, Qin Yang, Shuai Tan, Peng Xue, Xianhai Yang
Summary: The study proposed a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) to address the issues with WOA. Through experiments and comparisons, it was found that MHWOA outperformed other algorithms in terms of convergence speed and optimization performance, showing promising applications.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Biochemistry & Molecular Biology
Zachariah M. Ingram, Nathaniel W. Scull, David S. Schneider, Aaron L. Lucius
Summary: This article introduces a data analysis tool called MENOTR, which can overcome the initial guess dependence in parameter optimization, and demonstrates its functionality through several case studies. The article also clarifies the common use of NLLS optimization algorithms in biochemistry kinetic and thermodynamic research, as well as the issue of initial guess dependence.
BIOPHYSICAL CHEMISTRY
(2021)
Article
Computer Science, Artificial Intelligence
Bo Jiang, Hongtao Lei, Wenhua Li, Rui Wang
Summary: This paper investigates the design of hybrid renewable energy systems (HRES) and proposes a novel multi-objective evolutionary algorithm with a special environmental selection strategy to enhance the diversity of solutions. The effectiveness, superiority, and generalizability are validated through experiments and comparison with state-of-the-art algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Energy & Fuels
Yaolong Lu, Siqi Liang, Haibin Ouyang, Steven Li, Gai-ge Wang
Summary: Accurate estimation of model parameters is crucial for photovoltaic system simulation, evaluation, control, and optimization. This paper proposes a Hybrid Multi-Group Stochastic Cooperative Optimization algorithm (HMSCPSO) to improve the accuracy and reliability of the algorithm through a multi-group cooperative search mechanism.
Article
Engineering, Chemical
Changfang Yin, Guilian Liu
Summary: Integrating the reaction and extractive distillation system can reduce process costs. A systematic method is proposed to determine the optimal conditions, including solvent, solvent-to-feed ratio, extractive distillation sequence, and reactor operating conditions. This method can be used to evaluate different conditions for achieving the minimum total annualized cost.
Article
Computer Science, Information Systems
Mohammad Yassami, Payam Ashtari
Summary: Nowadays, there are many algorithms with different strengths and weaknesses, none of which is universally the best. In this study, a hybrid optimization algorithm called DHOA is proposed to combine the strengths of different algorithms and compensate for their weaknesses. The algorithm's performance is optimized using the parallel combination method, where the population size of the better algorithm is increased. Through the exploitation ability of PSO, HHO, and the crossover of GA, the proposed method achieves better and more accurate results in a shorter time.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Li Qiao, Kai Liu, Yanfeng Xue, Weidong Tang, Taybeh Salehnia
Summary: This paper presents a new hybrid optimization algorithm (AOA-HHO) for solving the multilevel thresholding image segmentation problem. The algorithm combines the features of arithmetic optimization algorithm and Harris hawks optimizer to obtain better thresholds in both local and global search, improving the accuracy and performance of image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Dezhen Zhang, Chenhao Gu, Hui Fang, Chengtao Ji, Xiuguo Zhang
Summary: A novel multi-strategy hybrid heuristic algorithm is proposed in this paper to achieve timely planning for clients within a limited time frame. The algorithm combines the strengths of two optimization strategies using a probabilistic model and a tree pruning strategy to improve efficiency and convergence.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Thermodynamics
Kai Fabian Kruber, Marius Krapoth, Tim Zeiner
FLUID PHASE EQUILIBRIA
(2017)
Article
Engineering, Chemical
Bettina Scharzec, Kai Fabian Kruber, Mirko Skiborowski
Summary: This study investigates the concentration of diluted aqueous streams in bio-based production processes using membrane separation technologies, as well as the importance of recovering valuable impurities before wastewater purification. By combining affinity-based separation processes and pressure-driven membrane separations, the economic performance of solvent recovery can be improved. The results suggest that this hybrid process has considerable economic savings potential.
CHEMICAL ENGINEERING SCIENCE
(2021)
Article
Engineering, Chemical
Kai Fabian Kruber, Mirko Skiborowski
Summary: The study presents a topology-based initialization and optimization approach for designing heteroazeotropic distillation processes. Different solvents with different mixture topologies are efficiently evaluated through systematic initialization, and this is further utilized for sensitivity analysis and multi-objective optimization. Three case studies are analyzed, resulting in a total of about 170 individually optimized process designs.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Thulasi Sasi, Kai Kruber, Moreno Ascani, Mirko Skiborowski
30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Kai Fabian Kruber, Hina Qammar, Mirko Skiborowski
30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Kai Fabian Kruber, Tamara Grueters, Mirko Skiborowski
29TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT A
(2019)
Proceedings Paper
Engineering, Environmental
Kai Fabian Kruber, Jan Scheffczyk, Kai Leonhard, Andre Bardow, Mirko Skiborowski
28TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING
(2018)
Article
Computer Science, Interdisciplinary Applications
Nohan Joemon, Melpakkam Pradeep, Lokesh K. Rajulapati, Raghunathan Rengaswamy
Summary: This paper introduces a smoothing-based approach for discovering partial differential equations from noisy measurements. The method is data-driven and improves performance by incorporating first principles knowledge. The effectiveness of the algorithm is demonstrated in a real system using a new benchmark metric.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhibin Lu, Yimeng Li, Chang He, Jingzheng Ren, Haoshui Yu, Bingjian Zhang, Qinglin Chen
Summary: This study proposes a new inverse design method using a physics-informed neural network to identify optimal heat sink designs. A hybrid PINN accurately approximates the governing equations of heat transfer processes, and a surrogate model is constructed for integration with optimization algorithms. The proposed method accelerates the search for Pareto-optimal designs and reduces search time. Comparing different scenarios facilitates real-time observation of multiphysics field changes, improving understanding of optimal designs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Luca Gasparini, Antonio Benedetti, Giulia Marchese, Connor Gallagher, Pierantonio Facco, Massimiliano Barolo
Summary: In this paper, a method for batch process monitoring with limited historical data is investigated. The methodology utilizes machine learning algorithms to generate virtual data and combines it with real data to build a process monitoring model. Automatic procedures are developed to optimize parameters, and indicators and metrics are proposed to assist virtual data generation activities.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Julia Jimenez-Romero, Adisa Azapagic, Robin Smith
Summary: Energy transition is a significant and complex challenge for the industry, and developing cost-effective solutions for synthesizing utility systems is crucial. The research combines mathematical formulation with realistic configurations and conditions to represent utility systems and provides a basis for synthesizing energy-efficient utility systems for the future.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Samuel Adeyemo, Debangsu Bhattacharyya
Summary: This work develops algorithms for estimating sparse interpretable data-driven models. The algorithms select the optimal basis functions and estimate the model parameters using Bayesian inferencing. The algorithms estimate the noise characteristics and model parameters simultaneously. The algorithms also exploit prior analysis and special properties for efficient pruning, and use a modified Akaike information criterion for model selection.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Abbasali Jafari-Nodoushan, Mohammad Hossein Dehghani Sadrabadi, Maryam Nili, Ahmad Makui, Rouzbeh Ghousi
Summary: This study presents a three-objective model to design a forward supply chain network considering interrelated operational and disruptive risks. Several strategies are implemented to cope with these risks, and a joint pricing strategy is used to enhance the profitability of the supply chain. The results show that managing risks and uncertainties simultaneously can improve sustainability goals and reduce associated costs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
T. A. Espaas, V. S. Vassiliadis
Summary: This paper extends the concept of higher-order search directions in interior point methods to convex nonlinear programming. It provides the mathematical framework for computing higher-order derivatives and highlights simplified computation for special cases. The paper also introduces a dimensional lifting procedure for transforming general nonlinear problems into more efficient forms and describes the algorithmic development required to employ these higher-order search directions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
David A. Linan, Gabriel Contreras-Zarazua, Eduardo Sanhez-Ramirez, Juan Gabriel Segovia-Hernandez, Luis A. Ricardez-Sandoval
Summary: This study proposes a parallel hybrid algorithm for optimal design of process flowsheets, which combines stochastic method with deterministic algorithm to achieve faster and improved convergence.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaoyong Lin, Zihui Li, Yongming Han, Zhiwei Chen, Zhiqiang Geng
Summary: A novel GAT-LSTM model is proposed for the production prediction and energy structure optimization of propylene production processes. It outperforms other models and can provide the optimal raw material scheme for actual production processes.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangoz, Ali Mesbah, Fani Boukouvala, Fernando Lima, Antonio del Rio Chanona, Christos Georgakis
Summary: This paper provides a concise perspective on the potential of machine learning in the PSE domain, based on discussions and talks during the FIPSE 5 conference. It highlights the need for domain-specific techniques in molecular/material design, data analytics, optimization, and control.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hesam Hassanpour, Prashant Mhaskar, Brandon Corbett
Summary: This work addresses the problem of designing an offset-free implementable reinforcement learning (RL) controller for nonlinear processes. A pre-training strategy is proposed to provide a secure platform for online implementations of the RL controller. The efficacy of the proposed approach is demonstrated through simulations on a chemical reactor example.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hunggi Lee, Donghyeon Lee, Jaewook Lee, Dongil Shin
Summary: This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, mitigating the risk of major industrial disasters, and providing faster and higher-resolution results.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Sibel Uygun Batgi, Ibrahim Dincer
Summary: This study examines the environmental impacts of three alternative hydrogen-generating processes and determines the best environmentally friendly option for hydrogen production by comparing different impact categories. The results show that the solar-based HyS cycle options perform the best in terms of global warming potential, abiotic depletion, acidification potential, ozone layer depletion, and human toxicity potential.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
LaGrande Gunnell, Bethany Nicholson, John D. Hedengren
Summary: A review of current trends in scientific computing shows a shift towards open-source and higher-level programming languages like Python, with increasing career opportunities in the next decade. Open-source modeling tools contribute to innovation in equation-based and data-driven applications, and the integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to significantly accelerate progress, but long-term support mechanisms are still necessary.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniel Cristiu, Federico d'Amore, Fabrizio Bezzo
Summary: This study presents a multi-objective mixed integer linear programming framework to optimize the supply chain for mixed plastic waste in Northern Italy. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximizing gross profit and minimizing greenhouse gas emissions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)