Prediction of optimum operating conditions of a furnace under uncertainty: An integrated framework of artificial neural network and genetic algorithm

Khan, Muzammil; Ahmad, Iftikhar; Ahsan, Muhammad; Kano, Manabu; Çalışkan, Hakan

doi:10.1016/j.fuel.2022.125563

Cited by 15 publications

(1 citation statement)

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“…The integration of machine learning into AI for exergy analysis has been explored in the literature. In [25], a Machine Learning (ML) model was devised to predict the exergy efficiency of a blast furnace. Subsequently, an Artificial Neural Network (ANN) model was developed to forecast exergy efficiency, relying on 11 uncertain process variables.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-assisted Prediction and Optimization of Exergy Efficiency and Destruction of Cumene Plant under Uncertainty

Ahmad,

Al-Khazaal

2024

Eng. Technol. Appl. Sci. Res.

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Machine Learning (ML)'s growing role in process industries during the digitalization era is notable. This study combines Artificial Neural Networks (ANNs) and Aspen Plus to predict exergy efficiency, exergy destruction, and potential improvements in a cumene plant under uncertain process conditions. An optimization framework, using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), was developed to enhance exergy efficiency amid uncertainty. Initially, a steady-state Aspen model evaluates exergy efficiency, irreversibility, and potential improvements. The proposed model is transitioned to a dynamic mode, introducing artificial uncertainties into key variables. An ANN model predicts exergy efficiency and exergy destruction under uncertainty. The PSO and GA-based optimization methods improve exergy efficiency and reduce exergy destruction. This work demonstrates the potential real-time application of intelligent methods in plant analysis.

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Section: Introductionmentioning

confidence: 99%