Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty

Al, Resul; Behera, Chitta Ranjan; Gernaey, Krist V.; Sin, Gürkan

doi:10.1016/j.compchemeng.2020.107118

Cited by 18 publications

(15 citation statements)

References 65 publications

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“…The methodology builds upon the evaluation of process simulations where high-fidelity models are used [26]. Furthermore, uncertainties can be easily included in a Monte Carlo-based approach [27].…”

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confidence: 99%

“…Several proposed frameworks utilize machine learning surrogate models as e.g. stochastic kriging (SK), coupled with a Bayesian optimization approach to iteratively improve the objective function results, leading to an optimal process design [27,28]. Despite this approach being able to incorporate complex physiological models of cell factories and processes, it is heavily limited by available computational power.…”

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confidence: 99%

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Synergistic optimization framework for the process synthesis and design of biorefineries

Vollmer

Gernaey

et al. 2021

Front. Chem. Sci. Eng.

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confidence: 99%

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confidence: 99%

Synergistic optimization framework for the process synthesis and design of biorefineries

Vollmer

Gernaey

et al. 2021

Front. Chem. Sci. Eng.

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“…Based on References [28,67], we have an unformed sensitive analysis by using Sobol indices. In the Sobol indices, the higher the Sobol indices values, the more influential the respective model parameters are.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The methodological framework proposed in the study supports a simulation-based optimization, as done in many studies [25][26][27][28]. A hybrid simulation-optimization framework to assess the risk in pipelines has been proposed [29].…”

Section: Introductionmentioning

confidence: 98%

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

2021

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The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent’s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent’s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent’s uncertain interactions and the dynamically changing structure.

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“…This potential for biogas generation could be translated as well to a source of renewable natural gas, which has specific composition requirements that demand high-tech sensors to assure its quality no matter its origin, as those developed in [15,16]. Due to their potential for resource recovery and the further implications in the water-food-energy nexus, WWTPs have been a research focus from different areas of Sensors 2022, 22, 1857 2 of 20 expertise: from modelling and engineering design [17][18][19][20][21][22][23][24] to process dynamics, simulation and integration [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Palma-Heredia

Verdaguer

Puig

et al. 2022

Sensors

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Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

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Stochastic simulation-based superstructure optimization framework for process synthesis and design under uncertainty

Cited by 18 publications

References 65 publications

Synergistic optimization framework for the process synthesis and design of biorefineries

Synergistic optimization framework for the process synthesis and design of biorefineries

A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

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