1
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Martín M, Taifouris M, Galán G. Lignocellulosic biorefineries: A multiscale approach for resource exploitation. BIORESOURCE TECHNOLOGY 2023:129397. [PMID: 37380036 DOI: 10.1016/j.biortech.2023.129397] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/22/2023] [Accepted: 06/24/2023] [Indexed: 06/30/2023]
Abstract
Biomass can become the source for chemicals towards a sustainable production system. However, the challenges it presents such as the variety of species, their widespread and sparse availability, and the expensive transportation claims for an integrated approach to design the novel production system. Multiscale approaches have not been properly extended to biorefineryes design and deployment, due to the comprehensive experimental and modelling work they require. A systems perspective provides the systematic framework to analyze the availability and composition of raw materials across regions, how that affects process design, the portfolio of products that can be obtained by evaluating the strong link between the biomass features and the process design. The use of lignocellulosic materials requires for a multidisciplinary work, that must lead to new process engineers with technical competences in biology, biotechnology but also process engineering, mathematics, computer science and social sciences towards a sustainable process/chemical industry.
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Affiliation(s)
- Mariano Martín
- Departamento de Ingeniería Química. Universidad de Salamanca. Pza. Caídos 1-5, 37008 Salamanca, Spain.
| | - Manuel Taifouris
- Departamento de Ingeniería Química. Universidad de Salamanca. Pza. Caídos 1-5, 37008 Salamanca, Spain
| | - Guillermo Galán
- Departamento de Ingeniería Química. Universidad de Salamanca. Pza. Caídos 1-5, 37008 Salamanca, Spain
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2
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Mamo T, Montastruc L, Negny S, Dendena L. INTEGRETED STRATEGIC AND TACTICAL OPTIMIZATION PLANNING OF BIOMASS TO BIOETHANOL SUPPLY CHAINS COUPLED WITH OPERATIONAL PLAN USING VEHICLE ROUTING: A CASE STUDY IN ETHIOPIA. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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3
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Chrisandina N, Vedant S, Iakovou E, Pistikopoulos E, El-Halwagi M. Multi-scale Integration for Enhanced Resilience of Sustainable Energy Supply Chains: Perspectives and Challenges. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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4
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Upscaling and Automation: Pushing the Boundaries of Multiscale Modeling through Symbolic Computing. Transp Porous Media 2021. [DOI: 10.1007/s11242-021-01628-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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5
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Skouteris A, Giannikopoulos I, Edgar TF, Baldea M, Allen DT, Stadtherr MA. Systems Analysis of Natural Gas Liquid Resources for Chemical Manufacturing: Strategic Utilization of Ethane. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alkiviadis Skouteris
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
| | - Ioannis Giannikopoulos
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
| | - Thomas F. Edgar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Austin, Texas 78712-1229, United States
| | - David T. Allen
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
- Center for Energy and Environmental Resources, The University of Texas at Austin, 10500 Exploration Way, Austin, Texas 78758, United States
| | - Mark A. Stadtherr
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton Street, Austin, Texas 78712-1589, United States
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6
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Costandy JG, Edgar TF, Baldea M. A Unified Reactor Network Synthesis Framework for Simultaneous Consideration of Batch and Continuous-Flow Reactor Alternatives. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c05799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Joseph G. Costandy
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas F. Edgar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Energy Institute, 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
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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Abstract
This paper describes the state of the art and future opportunities for process design and sustainable development. In the Introduction, the main global megatrends and the European Union’s response to two of them, the European Green Deal, are presented. The organization of professionals in the field, their conferences, and their publications support the two topics. A brief analysis of the published documents in the two most popular databases shows that the environmental dimension predominates, followed by the economic one, while the social pillar of sustainable development is undervalued. The main design tools for sustainability are described. As an important practical case, the European chemical and process industries are analyzed, and their achievements in sustainable development are highlighted; in particular, their strategies are presented in more detail. The conclusions cover the most urgent future development areas of (i) process industries and carbon capture with utilization or storage; (ii) process analysis, simulation, synthesis, and optimization tools, and (iii) zero waste, circular economy, and resource efficiency. While these developments are essential, more profound changes will be needed in the coming decades, such as shifting away from growth with changes in habits, lifestyles, and business models. Lifelong education for sustainable development will play a very important role in the growth of democracy and happiness instead of consumerism and neoliberalism.
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8
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Baratsas SG, Niziolek AM, Onel O, Matthews LR, Floudas CA, Hallermann DR, Sorescu SM, Pistikopoulos EN. A framework to predict the price of energy for the end-users with applications to monetary and energy policies. Nat Commun 2021; 12:18. [PMID: 33398000 PMCID: PMC7782726 DOI: 10.1038/s41467-020-20203-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 11/09/2020] [Indexed: 11/21/2022] Open
Abstract
Energy affects every single individual and entity in the world. Therefore, it is crucial to precisely quantify the “price of energy” and study how it evolves through time, through major political and social events, and through changes in energy and monetary policies. Here, we develop a predictive framework, an index to calculate the average price of energy in the United States. The complex energy landscape is thoroughly analysed to accurately determine the two key factors of this framework: the total demand of the energy products directed to the end-use sectors, and the corresponding price of each product. A rolling horizon predictive methodology is introduced to estimate future energy demands, with excellent predictive capability, shown over a period of 174 months. The effectiveness of the framework is demonstrated by addressing two policy questions of significant public interest. Global energy transformation requires quantifying the "price of energy" and studying its evolution. Here the authors present a predictive framework that calculates the average US price of energy, estimating future energy demands for up to four years with excellent accuracy, designing and optimizing energy and monetary policies.
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Affiliation(s)
- Stefanos G Baratsas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
| | - Alexander M Niziolek
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
| | - Onur Onel
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
| | - Logan R Matthews
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
| | - Christodoulos A Floudas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA
| | - Detlef R Hallermann
- Department of Finance, Mays Business School, Texas A&M University, College Station, TX, 77843, USA
| | - Sorin M Sorescu
- Department of Finance, Mays Business School, Texas A&M University, College Station, TX, 77843, USA
| | - Efstratios N Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, 77843, USA. .,Texas A&M Energy Institute, Texas A&M University, College Station, TX, 77843, USA.
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9
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Pedrozo H, Rodriguez Reartes S, Chen Q, Diaz M, Grossmann I. Surrogate-model based MILP for the optimal design of ethylene production from shale gas. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107015] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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10
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Zhu Q, Zhang B, Chen Q, He C, Foo DC, Ren J, Yu H. Model reductions for multiscale stochastic optimization of cooling water system equipped with closed wet cooling towers. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115773] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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12
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Demirhan CD, Tso WW, Powell JB, Heuberger CF, Pistikopoulos EN. A Multiscale Energy Systems Engineering Approach for Renewable Power Generation and Storage Optimization. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c00436] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- C. Doga Demirhan
- 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 77843-3372, United States
| | - William W. Tso
- 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 77843-3372, United States
| | - Joseph B. Powell
- Shell Technology Center, Royal Dutch Shell, Houston, Texas 77082, 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 77843-3372, United States
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13
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14
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Avraamidou S, Baratsas SG, Tian Y, Pistikopoulos EN. Circular Economy - A challenge and an opportunity for Process Systems Engineering. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106629] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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15
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Baumgärtner N, Bahl B, Hennen M, Bardow A. RiSES3: Rigorous Synthesis of Energy Supply and Storage Systems via time-series relaxation and aggregation. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.02.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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16
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17
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Abstract
Products from chemical engineering are essential for human well-being, but they also contribute to the degradation of ecosystem goods and services that are essential for sustaining all human activities. To contribute to sustainability, chemical engineering needs to address this paradox by developing chemical products and processes that meet the needs of present and future generations. Unintended harm of chemical engineering has usually appeared outside the discipline's traditional system boundary due to shifting of impacts across space, time, flows, or disciplines, and exceeding nature's capacity to supply goods and services. Being a subdiscipline of chemical engineering, process systems engineering (PSE) is best suited for ensuring that chemical engineering makes net positive contributions to sustainable development. This article reviews the role of PSE in the quest toward a sustainable chemical engineering. It focuses on advances in metrics, process design, product design, and process dynamics and control toward sustainability. Efforts toward contributing to this quest have already expanded the boundary of PSE to consider economic, environmental, and societal aspects of processes, products, and their life cycles. Future efforts need to account for the role of ecosystems in supporting industrial activities, and the effects of human behavior and markets on the environmental impacts of chemical products. Close interaction is needed between the reductionism of chemical engineering science and the holism of process systems engineering, along with a shift in the engineering paradigm from wanting to dominate nature to learning from it and respecting its limits.
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Affiliation(s)
- Bhavik R Bakshi
- Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, USA;
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18
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Zhang Q, Martín M, Grossmann IE. Integrated design and operation of renewables-based fuels and power production networks. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.06.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Demirhan CD, Tso WW, Powell JB, Pistikopoulos EN. Sustainable ammonia production through process synthesis and global optimization. AIChE J 2018. [DOI: 10.1002/aic.16498] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- C. Doga Demirhan
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station, TX 77843
- Texas A&M Energy Institute; Texas A&M University; College Station, TX 77843
| | - William W. Tso
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station, TX 77843
- Texas A&M Energy Institute; Texas A&M University; College Station, TX 77843
| | | | - Efstratios N. Pistikopoulos
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; College Station, TX 77843
- Texas A&M Energy Institute; Texas A&M University; College Station, TX 77843
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20
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Vapour–liquid equilibrium prediction for synthesis gas conversion using artificial neural networks. SOUTH AFRICAN JOURNAL OF CHEMICAL ENGINEERING 2018. [DOI: 10.1016/j.sajce.2018.10.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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21
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Abstract
Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.
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22
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Beykal B, Boukouvala F, Floudas CA, Pistikopoulos EN. Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization. Comput Chem Eng 2018; 116:488-502. [PMID: 30546167 PMCID: PMC6287910 DOI: 10.1016/j.compchemeng.2018.02.017] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.
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Affiliation(s)
- Burcu Beykal
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Fani Boukouvala
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Christodoulos A. Floudas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
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23
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Tso WW, Niziolek AM, Onel O, Demirhan CD, Floudas CA, Pistikopoulos EN. Reprint of: Enhancing natural gas-to-liquids (GTL) processes through chemical looping for syngas production: Process synthesis and global optimization. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN. Reprint of: Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.10.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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25
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Topolski K, Noureldin MM, Eljack FT, El-Halwagi MM. An anchor-tenant approach to the synthesis of carbon-hydrogen-oxygen symbiosis networks. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.02.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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26
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Municipal solid waste to liquid transportation fuels – Part III: An optimization-based nationwide supply chain management framework. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.10.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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27
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Matthews LR, Guzman YA, Floudas CA. Generalized robust counterparts for constraints with bounded and unbounded uncertain parameters. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Matthews LR, Guzman YA, Onel O, Niziolek AM, Floudas CA. Natural Gas to Liquid Transportation Fuels under Uncertainty Using Robust Optimization. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01638] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Logan R. Matthews
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
- 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
| | - Yannis A. Guzman
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
- 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
| | - Onur Onel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
- 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
| | - Alexander M. Niziolek
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States
- 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
| | - Christodoulos A. Floudas
- 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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29
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Schack D, Rihko-Struckmann L, Sundmacher K. Linear Programming Approach for Structure Optimization of Renewable-to-Chemicals (R2Chem) Production Networks. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b05305] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Dominik Schack
- Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Liisa Rihko-Struckmann
- Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Kai Sundmacher
- Department Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
- Department Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany
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30
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Tso WW, Niziolek AM, Onel O, Demirhan CD, Floudas CA, Pistikopoulos EN. Enhancing natural gas-to-liquids (GTL) processes through chemical looping for syngas production: Process synthesis and global optimization. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.03.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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31
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Mitsos A, Asprion N, Floudas CA, Bortz M, Baldea M, Bonvin D, Caspari A, Schäfer P. Challenges in process optimization for new feedstocks and energy sources. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.03.013] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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32
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Krishna SH, Huang K, Barnett KJ, He J, Maravelias CT, Dumesic JA, Huber GW, De bruyn M, Weckhuysen BM. Oxygenated commodity chemicals from chemo‐catalytic conversion of biomass derived heterocycles. AIChE J 2018. [DOI: 10.1002/aic.16172] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Siddarth H. Krishna
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - Kefeng Huang
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - Kevin J. Barnett
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - Jiayue He
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - Christos T. Maravelias
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - James A. Dumesic
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - George W. Huber
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
| | - Mario De bruyn
- Dept. of Chemical and Biological EngineeringUniversity of Wisconsin‐MadisonMadison WI 53706
- Faculty of Science, Debye Institute for Nanomaterials ScienceUtrecht University, Universiteitsweg 99CG Utrecht 3584 The Netherlands
| | - Bert M. Weckhuysen
- Faculty of Science, Debye Institute for Nanomaterials ScienceUtrecht University, Universiteitsweg 99CG Utrecht 3584 The Netherlands
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Bahl B, Lützow J, Shu D, Hollermann DE, Lampe M, Hennen M, Bardow A. Rigorous synthesis of energy systems by decomposition via time-series aggregation. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.01.023] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN. Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection. Comput Chem Eng 2018; 115:46-63. [PMID: 30386002 DOI: 10.1016/j.compchemeng.2018.03.025] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
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Affiliation(s)
- Melis Onel
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Chris A Kieslich
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Yannis A Guzman
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.,Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Christodoulos A Floudas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Efstratios N Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.,Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
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Burnak B, Katz J, Diangelakis NA, Pistikopoulos EN. Simultaneous Process Scheduling and Control: A Multiparametric Programming-Based Approach. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b04457] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Baris Burnak
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77845, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77845, United States
| | - Justin Katz
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77845, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77845, United States
| | - Nikolaos A. Diangelakis
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77845, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77845, United States
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77845, United States
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77845, United States
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Onel O, Niziolek AM, Butcher H, Wilhite BA, Floudas CA. Multi-scale approaches for gas-to-liquids process intensification: CFD modeling, process synthesis, and global optimization. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.01.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wilhite BA. Unconventional microreactor designs for process intensification in the distributed reforming of hydrocarbons: a review of recent developments at Texas A&M University. Curr Opin Chem Eng 2017. [DOI: 10.1016/j.coche.2017.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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38
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New a priori and a posteriori probabilistic bounds for robust counterpart optimization: III. Exact and near-exact a posteriori expressions for known probability distributions. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.03.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Moreno-Benito M, Agnolucci P, Papageorgiou LG. Towards a sustainable hydrogen economy: Optimisation-based framework for hydrogen infrastructure development. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2016.08.005] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Niziolek AM, Onel O, Floudas CA. Municipal solid waste to liquid transportation fuels, olefins, and aromatics: Process synthesis and deterministic global optimization. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2016.07.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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41
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New a priori and a posteriori probabilistic bounds for robust counterpart optimization: II. A priori bounds for known symmetric and asymmetric probability distributions. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2016.07.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Affiliation(s)
- Lorenz T. Biegler
- Dept. of Chemical Engineering; Carnegie Mellon University Pittsburgh; PA 15213
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Zhang BJ, Chen QL, Li J, Floudas CA. Operational strategy and planning for raw natural gas refining complexes: Process modeling and global optimization. AIChE J 2016. [DOI: 10.1002/aic.15416] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Bing J. Zhang
- School of Chemical Engineering and Technology, Guangdong Engineering Technology Research Center for Petrochemical Energy Conservation, Key Lab of Low-carbon Chemistry & Energy Conservation of Guangdong Province; Sun Yat-Sen University; No. 135, Xingang West Road Guangzhou 510275 China
| | - Qing L. Chen
- School of Chemical Engineering and Technology, Guangdong Engineering Technology Research Center for Petrochemical Energy Conservation, Key Lab of Low-carbon Chemistry & Energy Conservation of Guangdong Province; Sun Yat-Sen University; No. 135, Xingang West Road Guangzhou 510275 China
| | - Jie Li
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; 3122 TAMU College Station TX 77843
- Texas A&M Energy Institute, Texas A&M University; 3372 TAMU College Station TX 77843
| | - Christodoulos A. Floudas
- Artie McFerrin Dept. of Chemical Engineering; Texas A&M University; 3122 TAMU College Station TX 77843
- Texas A&M Energy Institute, Texas A&M University; 3372 TAMU College Station TX 77843
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