Article
Engineering, Aerospace
Brandon M. Lowe, David W. Zingg
Summary: This paper introduces a model order reduction framework for flutter-constrained aircraft optimization. By linearizing the Euler equations around a steady-state solution, a linear reduced-order model with fewer degrees of freedom is constructed and coupled with a linear structural model to form a monolithic aeroelastic system. The onset of flutter is determined by analyzing the eigenvalues of the resulting system, and the use of a stabilizing inner product is demonstrated to ensure the stability of the model.
Article
Engineering, Chemical
Jia Yu, Liqiang Lu, Xi Gao, Yupeng Xu, Mehrdad Shahnam, William A. Rogers
Summary: This article proposes a reduced-order modeling approach to accelerate high fidelity three-dimensional simulations of commercial-scale coal gasifiers by using quasi one-dimensional CFD-DEM simulation results as initial conditions. Experimental validation of the simulation results shows that final syngas composition and flowrates are strongly affected by operating conditions.
Article
Engineering, Chemical
Moritz Buchholz, Johannes Haus, Swantje Pietsch-Braune, Frank Kleine Jaeger, Stefan Heinrich
Summary: Due to the widespread use of spray dryers, the optimization and control of the process based on models are highly desired. In this study, a reduced order model based on a population balance approach is developed to accurately capture the shrinkage and drying mechanisms. The model is validated using experiments and suitable parameters are determined based on information from CFD simulations and previous droplet experiments. The results show good agreement between the model and experimental findings, highlighting the suitability of the population balance approach. Additionally, a novel method of incorporating trajectory information from detailed CFD simulations into the reduced order model is presented, improving model accuracy without increasing computational complexity.
ADVANCED POWDER TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Philip Pergam, Heiko Briesen
Summary: This study aims to improve the computational efficiency of a complex mathematical cake-filtration model with strong nonlinearities. A hybrid data-driven approach using proper orthogonal decomposition is employed, and optimal, globally defined basis functions are found based on a few sample simulations. The reduced-order model obtained from this approach has a 98% decrease in dimension compared to the full-order model, resulting in a 90% decrease in computational time for solving a benchmark optimization problem. This significant numerical speed-up offers the potential to use the reduced-order model in advanced process control and optimization methods.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Engineering, Aerospace
Yu-Hung Chang, Xingjian Wang, Liwei Zhang, Yixing Li, Simon Mak, Chien-Fu J. Wu, Vigor Yang
Summary: The study introduces a new surrogate model CKSPOD that integrates recent developments in various fields to emulate spatiotemporally evolving flows, outperforming traditional methods in flow dynamics cases.
Article
Computer Science, Interdisciplinary Applications
Vilmer Dahlberg, Anna Dalklint, Matthew Spicer, Oded Amir, Mathias Wallin
Summary: We propose an efficient computational approach for continuum topology optimization with linearized buckling constraints, using Reduced Order Models (ROM). A reanalysis technique is utilized to generate basis vectors, reducing the size of the generalized eigenvalue problems significantly. The approach is demonstrated through stiffness optimization with buckling constraints and shows promising results for various test cases. Based on the findings, we conclude that the ROM has the potential to save significant computational effort without compromising the quality of the results.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Engineering, Aerospace
Lukas Benjamin Inhestern, Dieter Peitsch, Guillermo Paniagua
Summary: This paper compares 2D URANS results with 1D Euler results to identify the governing equations for the acceleration and deceleration of the shock during the starting process. It characterizes the effect of pulsating flow frequency on the starting process and develops a reduced-order model coupled with an optimization algorithm for rapid designs with improved flow starting capability. The optimized design demonstrates dominant startability using URANS simulations. This method represents an essential tool for reducing time-to-market for aerospace propulsion components.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Mechanics
Jing Wang, Hairun Xie, Miao Zhang, Hui Xu
Summary: In this paper, a Physics-Assisted Variational Autoencoder is proposed to identify dominant features of transonic buffet, which combines unsupervised reduced-order modeling with additional physical information embedded via a buffet classifier. Statistical results reveal that the buffet state can be determined exactly with just one latent space when a proper weight of classifier is chosen. Based on this identification, the displacement thickness at 80% chordwise location is proposed as a metric for buffet prediction, achieving an accuracy of 98.5% in buffet state classification.
Article
Engineering, Chemical
Ming Li, Luchang Han, Xiao Luo, He'an Luo
Summary: In this study, kinetics experiments of the propylene chlorination fast reaction were conducted at low and high temperatures, proposing corresponding reaction mechanisms and models. It was found that radical mechanism occurs at high temperatures while molecular mechanism at low temperatures. By considering reversible reaction steps and hydrogen extraction processes, the proposed kinetics model shows good agreement with experimental data, introducing the concept of critical reaction temperature for determining dominant reaction mechanisms. The combination of high and low-temperature kinetics models in tubular reactor simulation can reflect the wide temperature variation influence and guide industrial reactor design.
Article
Nuclear Science & Technology
Huilun Kang, Zhaofei Tian, Guangliang Chen, Lei Li, Tianyu Wang
Summary: This paper proposes a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) to efficiently simulate the flow field in fuel rod bundles. A validated CFD model is established to output the flow field dataset, and the modes and coefficients of the flow field are extracted using the POD method. A deep feed-forward neural network is then selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. The flow field is reconstructed by combining the product of the POD basis and coefficients. Evaluation results show that the proposed POD-ROM accurately describes the flow status with high resolution in a few milliseconds.
NUCLEAR ENGINEERING AND TECHNOLOGY
(2022)
Article
Mathematics, Applied
Petar Mlinaric, Serkan Gugercin
Summary: We present a unified framework for G2-optimal reduced-order modeling of linear time-invariant dynamical systems and stationary parametric problems. By utilizing parameter-separable forms of the reduced-model quantities, we derive the gradients of the G2 cost function with respect to the reduced matrices, enabling a nonintrusive, data-driven, gradient-based descent algorithm that constructs the optimal approximant solely based on output samples. The framework covers both continuous (Lebesgue) and discrete cost functions by selecting an appropriate measure. Various numerical examples demonstrate the effectiveness of the proposed algorithm. Moreover, we analyze the conditions under which the data-driven approximant can be obtained through projection.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2023)
Article
Multidisciplinary Sciences
Anna Pietrenko-Dabrowska, Slawomir Koziel, Ubaid Ullah
Summary: Electromagnetic simulation tools are essential in contemporary antenna design, but the associated computational overhead can be a major setback. This paper proposes a novel modeling technique that incorporates response feature technology into the constrained modeling framework, allowing for accurate surrogates with small training data sets. The technique can be applied in other fields with costly simulation models.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Multidisciplinary
Xinshuai Zhang, Tingwei Ji, Fangfang Xie, Hongyu Zheng, Yao Zheng
Summary: This study proposes a novel compressed sensing reduced-order modeling framework for predicting unsteady flow fields from sparse and noisy sensor measurements. The framework includes an offline learning stage using Long Short Term Memory (LSTM) model and sparsity-promoting Dynamic Mode Decomposition (DMD) algorithm, and an online forecasting stage using Deep Neural Network (DNN) to establish correlations and predict flow fields accurately.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Agronomy
Fei Dai, Pengqing Xu, Zixiang Yuan, Ruijie Shi, Yiming Zhao, Xuefeng Song, Wuyun Zhao
Summary: The aim of this study was to investigate the effects of different working parameters on the cleaning efficiency of a cleaning device in a flax joint harvesting machine. The study used simulation and experimental verification to find the optimal combination of cleaning equipment parameters that met national and industry requirements, and confirmed the reliability of the model. The results of the study provide a reference for the design and performance optimization of flax combine cleaner.
Article
Engineering, Mechanical
Earl Dowell
Summary: This paper provides a personal account of the importance and significance of reduced-order models (ROM) in computational modeling. ROMs reduce the size and cost of the original model without losing accuracy. The motivation for creating a ROM is not only to reduce computational cost, but also to study a wider range of parameters and facilitate the interpretation of results, advancing our understanding of the model and the physical phenomena it describes.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Chemical
Kuan-Han Lin, John P. Eason, Lorenz T. Biegler
Summary: The study examines the impact of uncertainties on the control process in hydraulic fracturing and introduces the use of multistage NMPC as a solution to address parametric uncertainties, demonstrating its effective performance in constraint satisfaction.
Article
Engineering, Chemical
Anca Ostace, Yu-Yen Chen, Robert Parker, David S. Mebane, Chinedu O. Okoli, Andrew Lee, Andrew Tong, Liang-Shih Fan, Lorenz T. Biegler, Anthony P. Burgard, David C. Miller, Debangsu Bhattacharyya
Summary: Three kinetic models were developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier particle with CH4, CO, and H-2. Bayesian model building and parameter estimation framework were applied to quantify parameter and model structure uncertainty simultaneously. The final kinetic models showed excellent agreement between model predictions and calibration data, as well as new data not used for calibration.
CHEMICAL ENGINEERING SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Weifeng Chen, Baojia Wang, Lorenz T. Biegler
Summary: Parameter estimation is a crucial step in system modeling. However, in practice, the complexity of chemical engineering models and the interplay between parameters make it difficult to estimate all parameters accurately. This study proposes a parameter estimation approach based on a reduced Hessian matrix and statistical criteria, which improves model prediction by considering the influence of initial parameter values.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
K. Wang, L. T. Biegler
Summary: This paper develops solution strategies for large-scale nonsmooth optimization problems by transforming nonsmooth programs into equivalent mathematical programs with complementarity constraints (MPCCs) and devising NLP-based strategies for their solution. Relations between the solutions of these NLPs and of the MPCC is revealed by sensitivity analysis, and it is proved that the resulting solution of the NLP formulations are C- and M-stationary for the MPCCs in the limit.
OPTIMIZATION AND ENGINEERING
(2023)
Article
Engineering, Chemical
Xing Qian, Kuan-Han Lin, Shengkun Jia, Lorenz T. Biegler, Kejin Huang
Summary: This study develops nonlinear model predictive control (NMPC) schemes to control three-product DWCs, which are practical, effective, and promising technologies for distillation process intensification. Due to the strong interactivity and high nonlinearity of these systems, NMPC may be more suitable than traditional PI control. The model is established using Python and Pyomo platforms, and index reduction is used to avoid a high-index differential-algebraic equation (DAE) system. Case studies show that NMPC performs well in separating ethanol (A), n-propanol (B), and n-butanol (C) mixtures with small deviations and short settling times, demonstrating its feasibility and effectiveness for controlling three-product DWCs.
Article
Engineering, Chemical
Vibhav Dabadghao, Jaffer Ghouse, John Eslick, Andrew Lee, Anthony Burgard, David Miller, Lorenz Biegler
Summary: Vapor-liquid equilibrium (VLE) is a fundamental concept in computer-aided process engineering. Recent advancements in incorporating complementarity constraints have allowed VLE models to seamlessly handle phase transitions and supercritical processes. This study extends these models to equation-oriented frameworks and develops an efficient square-flash equation system implemented within the IDAES Integrated Platform.
Article
Automation & Control Systems
Yan Gao, Zhengyu Wei, Zhijiang Shao, Weifeng Chen, Zhengyu Song, Lorenz T. Biegler
Summary: This paper proposes an enhanced moving finite element method (EMFE) for optimization of finite element layout in trajectory optimization. The method takes the length of each finite element as a variable and utilizes the geometric estimation of non-collocation point error and generalized finite element length. The method demonstrates flexibility in problem reformulation, stability in numerical calculation, and the ability to locate breakpoints for singular control problems.
Article
Engineering, Chemical
Can Ekici, Christopher R. Ho, Joseph F. DeWilde, Lorenz T. Biegler, Paul M. Witt
Summary: This study presents the development and application of optimization strategies for mixed-catalyst, single-shot reactors in syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging due to poorly conditioned singular optimal control problems. The graded bed and partial-moving finite-element approaches were used to maximize the olefins yield. An increase of 1.3% in yield was achieved from one zone to three zones, and further improved by 0.2% to the infinite dimensional solution. These improvements can be implemented without additional investment. The results suggest the potential application of mixed-catalyst single shot reactor beds for enhancing reactor performance in other reaction mechanisms.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Engineering, Chemical
A. Pedrozo, C. M. Valderrama-Rios, M. Zamarripa, J. Morgan, J. P. Osorio-Suarez, A. Uribe-Rodriguez, M. S. Diaz, L. T. . Biegler
Summary: Post-combustion capture has the potential to mitigate climate change in the short term by reducing CO2 emissions. The most mature technology for this is chemical absorption-based processes, but costs need to be reduced for worldwide application. This study addresses the optimal design of absorption-based carbon capture technologies using the Aspen Plus platform in the equation-oriented mode.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Biographical-Item
Engineering, Chemical
Michael Baldea, Lorenz T. Biegler, Marianthi G. Ierapetritou
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Engineering, Chemical
Oscar Palma-Flores, Luis A. A. Ricardez-Sandoval, Lorenz T. T. Biegler
Summary: In this article, the integration problem of design and nonlinear model-based control under uncertainty and structural decisions is addressed. An algorithmic framework is proposed to determine the optimal location of process units or streams in closed-loop with a model-based controller. The problem is formulated as a mixed-integer bilevel problem (MIBLP) and transformed into a single-level mixed-integer nonlinear problem (MINLP) using a KKT transformation strategy. The methodology decomposes the MINLP into an integer-based master problem and primal subproblems, which are solved using a Discrete-Steepest Descent Algorithm (D-SDA). The discrete-based methodology is illustrated in a case study for a binary distillation column, where D-SDA outperforms the benchmark continuous-based formulation using differentiable distribution functions (DDFs).
Article
Computer Science, Interdisciplinary Applications
Robert B. Parker, Bethany L. Nicholson, John D. Siirola, Lorenz T. Biegler
Summary: Nonlinear modeling and optimization is valuable in decision-making for engineering practitioners, but programming these optimization problems based on complex processes can be time-consuming and prone to errors. The Dulmage-Mendelsohn decomposition is a tool that can detect and diagnose modeling errors by partitioning the bipartite graph of the system. This research provides background on the decomposition and its application to nonlinear optimization problems, demonstrates its use in diagnosing various modeling errors, and introduces software implementations for analyzing these problems in Pyomo and JuMP algebraic modeling languages.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Kuan-Han Lin, Lorenz T. Biegler
Summary: We propose a new economic nonlinear model predictive control (eNMPC) formulation that tracks the optimality conditions of the real-time optimization problem rather than any specific steady states. The proposed formulation maintains its nature of optimizing economic performance and assured stability properties with the Lyapunov inequality constraint for the closed-loop control. Under general assumptions, we prove that the proposed controller is asymptotically stable without process disturbances and is input-to-state stable when there is a process disturbance. The proposed eNMPC is demonstrated on two case studies and compared against setpoint-tracking NMPC with setpoints determined by the steady-state real-time optimizer to show improved dynamic performance. We also highlight the capability of self-stabilization of the new eNMPC with parameter updates in the process model.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Ruth Misener, Lorenz Biegler
Summary: This paper investigates the application of data-driven surrogate models in process optimization, discussing the requirements for robustness and accurate extrapolation and comparing the perspectives of surrogate-led and mathematical programming-led approaches. It also explores the verification problem and validates the effectiveness of surrogate-based optimization through two case studies.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Proceedings Paper
Automation & Control Systems
San Dinh, Kuan-Han Lin, Fernando V. Lima, Lorenz T. Biegler
Summary: In recent years, economic Nonlinear Model Predictive Control (eNMPC) has been recognized as a feasible alternative for distributed control systems. The proposed self-stabilizing eNMPC formulation does not require a pre-calculated steady-state condition and utilizes Lyapunov functions with embedded steady-state optimal conditions as additional constraints to achieve asymptotically stable behavior. The performance of this formulation is demonstrated with two case studies of a membrane reactor for natural gas utilization.
2023 AMERICAN CONTROL CONFERENCE, ACC
(2023)
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)
