Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition

Kim, Jeong-Dong; Joo, Chonghyo; Kim, Minsu; An, Nahyeon; Cho, Hyungtae; Moon, Il; Kim, Junghwan

doi:10.1016/j.eswa.2022.119464

Cited by 10 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normally, the hyperparameters including the model structure and optimizer have an impact on the model performance . Thus, much research has been reported about the hyperparameter tuning for their various systems. − In this study, the hyperparameters were tuned heuristically. , It consists of three hidden layers containing 100 nodes each. Furthermore, it utilizes the Adam optimizer and ReLU–ReLU–ReLU activation functions with 1000 epochs and a batch size of 16.…”

Section: Methodsmentioning

confidence: 99%

“…27−29 In this study, the hyperparameters were tuned heuristically. 12,17 It consists of three hidden layers containing 100 nodes each. Furthermore, it utilizes the Adam optimizer and ReLU−ReLU− ReLU activation functions with 1000 epochs and a batch size of 16.…”

Section: Shapley Additive Explanation (Shap)mentioning

confidence: 99%

“…The operational conditions of cracking furnaces (including COT) can be tuned based on their results. Kim et al suggested a multiobjective optimization model for a naphtha cracking furnace while considering uncertainties . They applied machine learning methods to their model with statistical methods to consider the uncertainties in the feed composition.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al suggested a multiobjective optimization model for a naphtha cracking furnace while considering uncertainties. 12 They applied machine learning methods to their model with statistical methods to consider the uncertainties in the feed composition. Most of the aforementioned studies have focused on finding the optimal operating conditions of cracking furnaces via multiobjective optimization.…”

Section: ■ Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Joo,

Kwon,

Lim

et al. 2024

ACS Sustainable Chem. Eng.

Self Cite

View full text Add to dashboard Cite

A naphtha-cracking furnace converts naphtha to ethylene (EL) and propylene (PL); the yields depend on the coil outlet temperature (COT) and naphtha composition. However, determining the optimal COT for maximizing net profit is difficult because the product price and its composition fluctuate frequently. Moreover, CO 2 emissions increase inevitably with increasing net profit, which requires taking environmental aspects into account. Hence, this study proposes a multiobjective optimization model for the naphtha cracking furnace by considering the incompatible goals: maximization of net profit and minimization of CO 2 emissions. First, a deep neural network (DNN)-based model is developed to predict the EL yield, PL yield, and CO 2 emissions for a given COT and naphtha composition using 783 industrial data points. Second, the developed model is combined with a nondominated sorting genetic algorithm (NSGA-II) for multiobjective optimization to obtain a Pareto front with various solutions. Finally, case studies are conducted for different product prices: EL was more expensive than PL in 2018; PL was more expensive than EL in 2019; and EL and PL had similar prices in 2020. For these three cases, the actual industrial data are applied to the model, and various solutions are proposed. The representative solutions in each case exhibit 5.35−6.14% higher net profits and 12.81−15.34% lower CO 2 emissions than those of the industrial data. The proposed model can help decision-makers by providing flexible options for the modification of various production parameters, including environmental regulations.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Shapley Additive Explanation (Shap)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Joo,

Kwon,

Lim

et al. 2024

ACS Sustainable Chem. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Over the past decade, ML techniques, including artificial neural networks (ANNs), have been successfully applied in various research fields, − including chemical engineering. Recently, the application and potential of ML for developing catalysts for the methane reforming reaction, , electrocatalysis, , water gas shift reaction, , oxidative coupling of methane reaction, − and selective catalytic reduction reaction have been reported. − …”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Roh,

Oh,

Lee

et al. 2024

ACS Sustainable Chem. Eng.

Self Cite

View full text Add to dashboard Cite

Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R 2 of 0.942 at 2478 epochs, whereas the ANN achieved an R 2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models.

show abstract

Development of a fast-charging protocol considering degradation using high-fidelity lithium-ion batteries

Kim,

Kim

2024

Computer Aided Chemical Engineering

View full text Add to dashboard Cite

Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition

Cited by 10 publications

References 42 publications

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Development of a fast-charging protocol considering degradation using high-fidelity lithium-ion batteries

Contact Info

Product

Resources

About

Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition

Cited by 10 publications

References 42 publications

Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Development of a fast-charging protocol considering degradation using high-fidelity lithium-ion batteries

Contact Info

Product

Resources

About

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm

Multiobjective Optimization of CO₂ Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic Algorithm