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1
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Cybersecurity and dynamic operation in practice: Equipment impacts and safety guarantees. J Loss Prev Process Ind 2022. [DOI: 10.1016/j.jlp.2022.104898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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2
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3
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Thebelt A, Wiebe J, Kronqvist J, Tsay C, Misener R. Maximizing information from chemical engineering data sets: Applications to machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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4
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Miao G, Zhuo K, Li G, Xiao J. An advanced optimization strategy for enhancing the performance of a hybrid pressure-swing distillation process in effective binary-azeotrope separation. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.120130] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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5
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Leo E, Engell S. Condition-based maintenance optimization via stochastic programming with endogenous uncertainty. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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6
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Gordon CAK, Pistikopoulos EN. Data‐driven
prescriptive maintenance toward
fault‐tolerant multiparametric
control. AIChE J 2021. [DOI: 10.1002/aic.17489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Christopher A. K. Gordon
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station Texas USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute Texas A&M University College Station Texas USA
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7
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Feng W, Feng Y, Zhang Q. Multistage distributionally robust optimization for integrated production and maintenance scheduling. AIChE J 2021. [DOI: 10.1002/aic.17329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Wei Feng
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering Zhejiang University Hangzhou China
| | - Yiping Feng
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering Zhejiang University Hangzhou China
| | - Qi Zhang
- Department of Chemical Engineering and Materials Science University of Minnesota Minneapolis Minnesota USA
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8
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Gordon CAK, Burnak B, Onel M, Pistikopoulos EN. Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03241] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Christopher Ampofo Kwadwo Gordon
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
- Mary Kay O’Connor Process Safety Center, College Station, Texas 77843, United States
| | - Baris Burnak
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
| | - Melis Onel
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
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9
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Castro PM, Dalle Ave G, Engell S, Grossmann IE, Harjunkoski I. Industrial Demand Side Management of a Steel Plant Considering Alternative Power Modes and Electrode Replacement. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01714] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Pedro M. Castro
- Centro de Matemática Aplicações Fundamentais e Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Giancarlo Dalle Ave
- Hitachi ABB Power Grids Research, Kallstadter Str. 1, 68309 Mannheim, Germany
- Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Emil-Figge-Str. 70, 44221 Dortmund, Germany
| | - Sebastian Engell
- Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Emil-Figge-Str. 70, 44221 Dortmund, Germany
| | - Ignacio E. Grossmann
- Center for Advanced Process Decision-Making, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Iiro Harjunkoski
- Hitachi ABB Power Grids Research, Kallstadter Str. 1, 68309 Mannheim, Germany
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10
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Letsios D, Baltean-Lugojan R, Ceccon F, Mistry M, Wiebe J, Misener R. Approximation algorithms for process systems engineering. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106599] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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11
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Tsay C, Baldea M. 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02282] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Calvin Tsay
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
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12
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Affiliation(s)
- Johannes Wiebe
- Department of Computing, Imperial College London, London, SW7 2AZ, U.K
| | - Inês Cecı́lio
- Schlumberger Cambridge Research, Cambridge, CB3 0EL, U.K
| | - Ruth Misener
- Department of Computing, Imperial College London, London, SW7 2AZ, U.K
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13
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Durand H. On accounting for equipment-control interactions in economic model predictive control via process state constraints. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.01.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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14
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Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9071287] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.
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