TY - JOUR AU - Cole, D.L. AU - Zavala, V.M. TI - PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 185 PY - 2024 SN - 0098-1354 DO - 10.1016/j.compchemeng.2024.108679 UR - https://m2.mtmt.hu/api/publication/34797994 ID - 34797994 N1 - Export Date: 18 April 2024 CODEN: CCEND Correspondence Address: Zavala, V.M.; Department of Chemical and Biological Engineering, United States; email: victor.zavala@wisc.edu LA - English DB - MTMT ER - TY - JOUR AU - Bublitz, Saskia AU - Esche, Erik AU - Repke, Jens-Uwe TI - Automatic initial value generation procedure for nonlinear process models by interval arithmetic based cutting and splitting JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - & PY - 2024 SP - 108672 SN - 0098-1354 DO - 10.1016/j.compchemeng.2024.108672 UR - https://m2.mtmt.hu/api/publication/34753556 ID - 34753556 LA - English DB - MTMT ER - TY - JOUR AU - Che, Xinhao AU - Liu, Qilei AU - Yu, Fang AU - Zhang, Lei AU - Gani, Rafiqul TI - A virtual screening framework based on the binding site selectivity for small molecule drug discovery JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 184 PY - 2024 SN - 0098-1354 DO - 10.1016/j.compchemeng.2024.108626 UR - https://m2.mtmt.hu/api/publication/34747054 ID - 34747054 LA - English DB - MTMT ER - TY - JOUR AU - Bernal, Neira D.E. AU - Laird, C.D. AU - Lueg, L.R. AU - Harwood, S.M. AU - Trenev, D. AU - Venturelli, D. TI - Utilizing modern computer architectures to solve mathematical optimization problems: A survey JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 184 PY - 2024 SN - 0098-1354 DO - 10.1016/j.compchemeng.2024.108627 UR - https://m2.mtmt.hu/api/publication/34735264 ID - 34735264 N1 - Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, United States Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States ExxonMobil Technology and Engineering Company - Research, Clinton, NJ, United States Research Institute for Advanced Computer Science, Universities Space Research Association, Mountain View, CA, United States Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center, Moffett Field, CA, United States Export Date: 13 March 2024 CODEN: CCEND Correspondence Address: Laird, C.D.; Department of Chemical Engineering, United States; email: claird@andrew.cmu.edu Funding details: National Aeronautics and Space Administration, NASA, NNA16BD14C, SAA2-403506 Funding details: Carnegie Mellon University, CMU, FOCAPO/CPC 2023 Funding text 1: DBN and DV were supported via the NASA Academic Mission Services, United States contract NNA16BD14C funded under SAA2-403506. CDL and LRL would like to acknowledge partial support from the Department of Chemical Engineering at Carnegie Mellon University, United States. This full-length article was requested as an extension of the conference paper presented at the Foundations of Computer Aided Process Optimization/Chemical Process Control 2023 (FOCAPO/CPC 2023) titled “Impact of Emerging Computing Architectures and Opportunities for Process Systems Engineering Applications”. This full-length article is focused on mathematical programming and has added significant material for HPC and quantum computing. Funding text 2: DBN and DV were supported via the NASA Academic Mission Services, United States contract NNA16BD14C funded under SAA2-403506 . CDL and LRL would like to acknowledge partial support from the Department of Chemical Engineering at Carnegie Mellon University, United States . This full-length article was requested as an extension of the conference paper presented at the Foundations of Computer Aided Process Optimization/Chemical Process Control 2023 (FOCAPO/CPC 2023) titled “Impact of Emerging Computing Architectures and Opportunities for Process Systems Engineering Applications”. This full-length article is focused on mathematical programming and has added significant material for HPC and quantum computing. AB - Numerical algorithms to solve mathematical optimization problems efficiently are essential to applications in many areas of engineering and computational science. To solve optimization problems of ever-increasing scale and complexity, we need methods that exploit emerging hardware systems. However, the complexities of specific architectures and their impact on performance can be challenging. This article provides an overview of emerging hardware architectures and how they are used to solve mathematical optimization problems. We focus on parallel high-performance computing architectures, which are well-established yet challenging to employ for optimization, as well as digital quantum computing, which has recently gained attention due to its potential for transformative computational performance. Furthermore, we highlight several other emerging hardware architectures that may become relevant for mathematical optimization. We intend for this review to encourage the optimization and process engineering communities to increasingly consider both hardware and software developments in the pursuit of superior computational performance. © 2024 LA - English DB - MTMT ER - TY - JOUR AU - Salehi, Yousef AU - Chiplunkar, Ranjith AU - Huang, Biao TI - Robust-to-occlusion machine vision model for predicting quality variables with slow-rate measurements JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 182 PY - 2024 PG - 9 SN - 0098-1354 DO - 10.1016/j.compchemeng.2023.108581 UR - https://m2.mtmt.hu/api/publication/34669265 ID - 34669265 LA - English DB - MTMT ER - TY - JOUR AU - Tian, Huayu AU - Jagana, Jnana Sai AU - Zhang, Qi AU - Ierapetritou, Marianthi TI - Feasibility/Flexibility-based optimization for process design and operations JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 180 PY - 2024 PG - 10 SN - 0098-1354 DO - 10.1016/j.compchemeng.2023.108461 UR - https://m2.mtmt.hu/api/publication/34649520 ID - 34649520 LA - English DB - MTMT ER - TY - JOUR AU - Khuat, Thanh Tung AU - Bassett, Robert AU - Otte, Ellen AU - Grevis-James, Alistair AU - Gabrys, Bogdan TI - Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 182 PY - 2024 PG - 59 SN - 0098-1354 DO - 10.1016/j.compchemeng.2024.108585 UR - https://m2.mtmt.hu/api/publication/34648701 ID - 34648701 N1 - Complex Adaptive Systems Laboratory, The Data Science Institute, University of Technology SydneyNSW 2007, Australia CSL Innovation, Melbourne, VIC 3000, Australia Export Date: 29 February 2024 CODEN: CCEND Correspondence Address: Khuat, T.T.; Complex Adaptive Systems Laboratory, Australia; email: Thanhtung.Khuat@uts.edu.au Funding details: Australian Research Council, ARC, IH210100051 Funding details: University of Technology Sydney, UTS Funding details: RMIT University, RMIT Funding details: University of Melbourne, UNIMELB Funding text 1: This research was supported under the Australian Research Council's Industrial Transformation Research Program (ITRP) funding scheme (project number IH210100051). The ARC Digital Bioprocess Development Hub is a collaboration between The University of Melbourne, University of Technology Sydney, RMIT University, CSL Innovation Pty Ltd, Cytiva (Global Life Science Solutions Australia Pty Ltd) and Patheon Biologics Australia Pty Ltd. Funding text 2: This research was supported under the Australian Research Council’s Industrial Transformation Research Program (ITRP) funding scheme (project number IH210100051 ). The ARC Digital Bioprocess Development Hub is a collaboration between The University of Melbourne, University of Technology Sydney, RMIT University, CSL Innovation Pty Ltd, Cytiva (Global Life Science Solutions Australia Pty Ltd) and Patheon Biologics Australia Pty Ltd. LA - English DB - MTMT ER - TY - JOUR AU - Adeyemo, Samuel AU - Bhattacharyya, Debangsu TI - Optimal nonlinear dynamic sparse model selection and Bayesian parameter estimation for nonlinear systems JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 180 PY - 2024 PG - 15 SN - 0098-1354 DO - 10.1016/j.compchemeng.2023.108502 UR - https://m2.mtmt.hu/api/publication/34632016 ID - 34632016 LA - English DB - MTMT ER - TY - JOUR AU - Cristiu, Daniel AU - d'Amore, Federico AU - Bezzo, Fabrizio TI - Economic and environmental optimisation of mixed plastic waste supply chains in Northern Italy comparing incineration and pyrolysis technologies JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 180 PY - 2024 PG - 16 SN - 0098-1354 DO - 10.1016/j.compchemeng.2023.108503 UR - https://m2.mtmt.hu/api/publication/34612234 ID - 34612234 AB - In the quest for sustainable plastic waste management, understanding economic and environmental implications enables optimal selection of treatment technologies. This study presents a multi-objective mixed integer linear programming framework to optimise the supply chain for mixed plastic waste in Northern Italy. Two technologies are considered: incineration and pyrolysis. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximising gross profit and minimising greenhouse gas (GHG) emissions. Economic optimisation favours incineration for treating mixed plastic waste, resulting in the highest gross profit of 115 Meuro per year, and the highest net GHG emissions of about 680 kt CO2eq per year. When the aim is environmental optimisation, pyrolysis is preferred due to its lower GHG emissions of 387 kt of CO2eq per year and yielding a gross profit of 54 Meuro per year. Trade-off Pareto optimal solutions were analysed to identify reasonable trade-off configurations between the two objectives. LA - English DB - MTMT ER - TY - JOUR AU - Shirazaki, Setayesh AU - Pishvaee, Mir Saman AU - Sobati, Mohammad Amin TI - Integrated supply chain network design and superstructure optimization problem: A case study of microalgae biofuel supply chain JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 180 PY - 2024 PG - 19 SN - 0098-1354 DO - 10.1016/j.compchemeng.2023.108468 UR - https://m2.mtmt.hu/api/publication/34609095 ID - 34609095 AB - In recent years, several strategies have been developed and adopted in a bid to manage the biofuel supply chain. In this paper, a two-stage optimization model is proposed for integrated microalgae biofuel supply chain network design and superstructure optimization problems. In the first stage, the design of the carbon capture, utilization, and storage (CCUS) network is taken into account. A robust mixed integer linear programming (RMILP) model is proposed to optimize the strategic CCUS decisions, including the simultaneous selection of emission sources, capture facilitates, Carbon Dioxide (CO2) pipelines, intermediate transportation sites and storage sites, or microalgae cultivation sites. The second stage is dedicated to biorefinery superstructure optimization in order to determine the optimal/promising biorefinery configurations. The presented model is able to handle the inherent uncertainty of critical input parameters. Moreover, the results show that biodiesel production cost cannot compete with current diesel price, but it can be reduced significantly by improving biomass productivity. LA - English DB - MTMT ER -