A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors

Ani, Zainab Al; Gujarathi, Ashish M.; Al-Muhtaseb, Ala’a H.

doi:10.1007/s10462-022-10219-z

Cited by 12 publications

(2 citation statements)

References 310 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, regarding optimization problems, evolutionary algorithms with swarm intelligence methods are applied in many fields, from biology [20], chemistry [21,22], energy production [23][24][25], neural networks training and design [26][27][28], to humanities [29]. This field was revolutionized by the development of two families of methods in 1995: Differential Evolution (DE) [30] and Particle Swarm Optimization (PSO) [31].…”

Section: Related Workmentioning

confidence: 99%

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Birdal

2024

Environ. Res. Commun.

View full text Add to dashboard Cite

Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather conditions, historical load data, and seasonality. The accuracy of electricity load forecasting improved by incorporating air pollution data and its potential effects, especially in regions where air quality heavily impacts energy consumption and generation patterns. This robust prediction model is capable of capturing the complex interactions between air pollution and electricity load by integrating innovative environmental factors with historical load data, weather forecasts, and other features. As part of the second contribution, we use metaheuristic algorithms to optimize hyper parameters, which provide advantages such as exploration capability, global optimization, robustness, parallelization, and adaptability making them valuable tools to improve machine learning models’ performance and efficiency. The study found that the correlation coefficient (R) between predicted and real electricity demand and supply was high, at 0.9911. Beyond that this approach reduces MAPE by up to 19.5% when CNN and particle swarm optimization are combined with utilizing innovative air pollution variables. As a result, the optimization results were robust compared to state-of-the-art results based on statistical metrics such as RMSE and MAPE. Lastly, we emphasize the importance of factoring in air pollution effects when forecasting and managing electricity loads; future research directions include developing integrated modeling frameworks that reflect the dynamic interaction between air quality, energy consumption, and renewable energy generation.

show abstract

Section: Related Workmentioning

confidence: 99%

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Birdal

2024

Environ. Res. Commun.

View full text Add to dashboard Cite

show abstract

“…Although capable of effectively handling multi-objective optimization problems, evolutionary and optimization-only algorithms still have a number of challenges, including, but not limited to, the speed of convergence of the algorithms, the diversity of solutions, and the interpretability of the algorithms [3].…”

Section: Problems With the Studymentioning

confidence: 99%

Optimization Strategy for Industrial Machinery Product Selection Scheme Based on DMOEA

Liu,

Yang,

Huang

2024

IJACSA

View full text Add to dashboard Cite

With the continuous innovation and replacement of industrial machinery products, the traditional optional configuration plans are no longer able to complete product selection work with high quality. To further optimize the product selection process and solve the multi-objective selection problem of industrial machinery products, a multi-objective problem model for product selection is normalized and constructed based on the existing difficulties in industrial machinery product selection. A new product selection model is proposed by introducing a multi-objective evolutionary algorithm based on density calculation for model solving. The experimental results showed that the new model had the highest selection success rate of 97% and selection accuracy close to 95% when the iterations were 250. In addition, the maximum absolute error sum of the selected bearing and bearing seat diameters under this model was 0.002. The maximum relative error was 0.01%. The highest reliability of algorithm fitting was 99.9%. Simulation tests found that the average selection success rate was 93%. The average selection quality loss was 26%. In summary, the new selection model proposed in the study has certain advantages and feasibility. It can provide effective decision-making solutions for the design and selection of industrial machinery products.

show abstract

Optimization and safety analysis for CO oxidative coupling reactor with co-current thermal management

Chi,

Liu,

et al. 2024

Chemical Engineering Science

View full text Add to dashboard Cite

A state of art review on applications of multi-objective evolutionary algorithms in chemicals production reactors

Cited by 12 publications

References 310 publications

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization

Optimization Strategy for Industrial Machinery Product Selection Scheme Based on DMOEA

Optimization and safety analysis for CO oxidative coupling reactor with co-current thermal management

Contact Info

Product

Resources

About