首页|Guangxi University of Finance and Economics Reports Findings in Artificial Intel ligence (Development of novel computational models based on artificial intellige nce technique to predict liquids mixtures separation via vacuum membrane distill ation)
Guangxi University of Finance and Economics Reports Findings in Artificial Intel ligence (Development of novel computational models based on artificial intellige nce technique to predict liquids mixtures separation via vacuum membrane distill ation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Nanni ng, People's Republic of China, by NewsRx editors, the research stated, "The fun damental objective of this paper is to use Machine Learning (ML) methods for bui lding models on temperature (T) prediction using input features r and z for a me mbrane separation process. A hybrid model was developed based on computational f luid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models." Our news editors obtained a quote from the research from the Guangxi University of Finance and Economics, "The CFD simulations were performed to estimate temper ature distribution in a vacuum membrane distillation (VMD) process for separatio n of liquid mixtures. The evaluated ML models include Support Vector Machine (SV M), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution ( DE) for hyper-parameter tuning, and model validation was performed using Monte C arlo Cross-Validation. The results clearly indicated the models' effectiveness i n temperature prediction, with SVM outperforming other models in terms of accura cy. The SVM model had a mean R value of 0.9969 and a standard deviation of 0.000 1, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks."
NanningPeople's Republic of ChinaAsiaArtificial IntelligenceCyborgsEmerging TechnologiesMachine LearningSu pport Vector Machines
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