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Aghayev Z, Szafran AT, Tran A, Ganesh HS, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN, Beykal B. Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data. Chem Eng Sci 2023; 281:119086. [PMID: 37637227 PMCID: PMC10448728 DOI: 10.1016/j.ces.2023.119086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.
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Affiliation(s)
- Zahir Aghayev
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
| | - Adam T. Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
| | - Anh Tran
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Hari S. Ganesh
- Discipline of Chemical Engineering, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, Gujarat - 382055, India
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Lan Zhou
- Department of Statistics, Texas A&M University, College Station, TX
| | - Michael A. Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
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Li H, Zhang S, Qiu T. Receding Horizon Optimization of Ethylene Cracking Operation and Scheduling under Supply Chain Fluctuations. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Affiliation(s)
- Haoran Li
- Institute of Process Systems Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
| | - Shuyuan Zhang
- Institute of Process Systems Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
| | - Tong Qiu
- Institute of Process Systems Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
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Zinare T, Di Pretoro A, Chiari V, Montastruc L, Negny S. Benefits of feasibility constrained sampling on unit operations surrogate model accuracy. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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Ren Y, Liao Z, Yang Y, Sun J, Jiang B, Wang J, Yang Y. Direct prediction of steam cracking products from naphtha bulk properties: Application of the two sub-networks ANN. FRONTIERS IN CHEMICAL ENGINEERING 2022. [DOI: 10.3389/fceng.2022.983035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.
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Bi K, Yan M, Zhang S, Qiu T. Three-scale integrated optimization model of furnace simulation, cyclic scheduling, and supply chain of ethylene plants. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2021.03.038] [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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Di Pretoro A, Bruns B, Negny S, Grünewald M, Riese J. Demand Response Scheduling Using Derivative-Based Dynamic Surrogate Models. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Beykal B, Avraamidou S, Pistikopoulos EN. Data-Driven Optimization of Mixed-integer Bi-level Multi-follower Integrated Planning and Scheduling Problems Under Demand Uncertainty. Comput Chem Eng 2022; 156:107551. [PMID: 34720250 PMCID: PMC8553017 DOI: 10.1016/j.compchemeng.2021.107551] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. In this paper, we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.
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Affiliation(s)
- Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Styliani Avraamidou
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Efstratios N. Pistikopoulos
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
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Zhao G, Yang M, Dua W, Shena F, Qian F. A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2021.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Bi K, Zhang S, Zhang C, Li H, Huang X, Liu H, Qiu T. Knowledge expression, numerical modeling and optimization application of ethylene thermal cracking: From the perspective of intelligent manufacturing. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2021.03.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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