Drier Bed Adsorption Predictive Model with Enhancement of Long Short-Term Memory and Particle Swarm Optimization

• Published in: International Journal of Advanced Computer Science and Applications
• Main Author: Yusoff M.; Sallehud-din M.T.M.; Tahir N.; Yaacob W.F.W.; Maarof N.N.N.A.; Zain J.M.; Azmi P.A.R.; Karunakumar C.
• Format: Article
• Language: English
• Published: Science and Information Organization 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184662823&doi=10.14569%2fIJACSA.2023.0141206&partnerID=40&md5=823c2e5e44148488b390a983b3508233

—The drier bed adsorption processes remove moisture from gases and liquids by ensuring product quality, extending equipment lifespan, and enhancing safety in various applications. The longevity of adsorption beds is quantified by net loading capacity values that directly impact the effectiveness of the moisture removal process. Predictive modeling has emerged as a valuable tool to enhance drier bed adsorption systems. Despite the increasing significance of predictive modeling in enhancing the efficiency of drier bed adsorption processes, the existing methodologies frequently exhibit deficiencies in accuracy and flexibility, which are crucial for optimizing process performance. This research investigates the effectiveness of a hybrid approach combining Long Short-Term Memory and Particle Swarm Optimization (LSTM+PSO) as a proposed method to predict the net loading capacity of a drier bed. The train-test split ratios and rolling origin technique are explored to assess model performance. The findings reveal that LSTM+PSO with a 70:30 train-test split ratio outperform other methods with the lowest error. Bed 1 exhibits an RMSE of 1.31 and an MSE of 0.91, while Bed 2 archives RMSE and MSE values of 0.81 and 0.72, respectively and Bed 3 with an RMSE of 0.19 and an MSE of 0.13, followed by Bed 4 with an RMSE of 0.67 and an MSE of 0.36. Bed 5 exhibits an RMSE of 0.42 and an MSE of 0.34. Furthermore, this research compares LSTM+PSO with LSTM and conventional predictive methods: Support Vector Regression, Seasonal Autoregressive Integrated Moving Average with Exogenous Variables, and Random Forest. © (2023), (Science and Information Organization). All Rights Reserved.