Iranian Chemical Engineering Journal

Iranian Chemical Engineering Journal

Measuring the Quality of Industrial Water Used in a Chemical Plant Using arTificial Intelligence (Logistic Regression, Naive Bayesian, Support Vector Machine, Random Forest, and Decision Tree)

Document Type : Original Article

Authors
F. Sarmast Alizadeh 1
N. Esfandiari 2
1 M. Sc. Student, Department of Chemical Engineering, Marvdasht Branch,Islamic Azad University, Marvdasht, Iran
2 Associate Professor, Department of Chemical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
10.22034/ijche.2023.408953.1334
Abstract
Continuous measurement of factory water quality is important. Current methods of measuring water quality are not efficient enough. In this research, a new method using the concepts of artificial intelligence and machine learning has been proposed to solve the mentioned challenges. The proposed research method has been trained and validated using 472 samples of chemical data in MATLAB software. Each data sample has 6 input attributes (pH, conductivity, water hardness, total water-soluble solids, free chlorine, and alkalinity) and one output attribute (target).
The parameters of disturbance matrix, precision, accuracy, and readability have been used to evaluate the efficiency of water quality measurement. The highest accuracy is related to the random forest method. The decision tree, simple Bayes, and vector machine methods are the same. The most refreshing rate is related to the decision tree method. The artificial intelligence method of the proposed decision tree with an accuracy equal to 70%, accuracy equal to 98%, and recall equal to 96% compared to logistic regression methods, Naive Bayesian method, support vector machine, and random forest, shows more efficiency and less error.
Keywords
Quality
Industrial Water
Chemical Plant
Artificial Intelligence
Subjects
[1]        Torabian, A., Hasani, A. H., Rahmanipour, A., & Mahjouri, M. (2009). Guidance on quality classification of raw water, wastewater and return water for industrial and recreational use. Planning and Budgeting of the Country, 462. In Persian.
[2]        Rokach, L., & Oded, M. (2014). Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing Co., Inc.1060 Main Street Suite 1B River Edge, NJ United States.
[3]        Lee, J. H., Lee, J. Y., Lee, M. H., Lee, M. Y., Kim, Y. W., Hyung, J. S, Kim, K. B., Cha, Y. K., & Koo, J. Y. (2022). Development of a short-term water quality prediction model for urban rivers using real-time water quality data. Water Supply, 22 (4), 4082–4097.
[4]        Giudici, P., Centurelli, M., & Turchetta, S. (2024). Artificial Intelligence risk measurement. Expert Systems with Applications, 235, 121220.
[5]        Wang, S., Ren, J., & Bai, R. (2023). A semi-supervised adaptive discriminative discretization method improving discrimination power of regularized naive Bayes. Expert Systems with Applications, 225, 120094.
[6]        Lou, C., & Xie, X. (2023). Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data. Neurocomputing, 549, 126458.
[7]        Yousefi, M., & Yasin, M. (2015). Optimization of Industrial Water Pretreatment Operational Processes Using Artificial Neural Network and Genetic Algorithm. Iranian Chemical Engineering Journal, 14 (79), 13-22. In Persian.
[8]        Azizi, S., & Karimi, H. (2016). Prediction of horizontal liquid-liquid two-phase flow patterns using artificial neural networkIanian. Chemical Engineering Journal, 14 (82), 65-74. In Persian.
[9]        Khan, M. S., Islam, I. N., Uddin, J., Islam, S., & Nasir, M. K. (2022). Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. Journal of King Saud University-Computer and Information Sciences, 34, 4781-4773.
[10]      Azrour, M., Mabrouki, J., Fattah, G., Guezzaz, A., & Aziz, F. (2022). Machine learning algorithms for efficient water quality prediction. Modeling Earth Systems and Environment, 8, 2801-2793.
[11]      Wu, J., & Wang, Z. (2022). A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water, 14 (4), 610.
[12]      Prasad, D. V. V., Venkataramana, L. Y., Kumar, P. S., Prasannamedha, G., Harshana, S., Srividya, S. J., Harrinei, K., & Indraganti, S. (2022). Analysis and prediction of water quality using deep learning and auto deep learning techniques. Science of The Total Environment, 821, 153311.
[13]      Galster, H. (1991). pH measurement. VCH (Verlagsgesellschaft), New York.
[14]      Light, T. S., Licht, S., Bevilacqua, A. C., & Morash, K. R. (2004). The fundamental conductivity and resistivity of water. Electrochemical and solid-state letters, 8 (1), 16.
[15]      Lerga, T. M., & O'Sullivan, C. K. (2008). Rapid determination of total hardness in water using fluorescent molecular aptamer beacon. analytica chimica acta, 610, 105-111.
[16]      Wang, B. B. (2021). Research on drinking water purification technologies for household use by reducing total dissolved solids (TDS). Plos one, 16, e0257865.
[17]      Aoki, T., & Munemori, M. (1983). Continuous flow determination of free chlorine in water. Analytical Chemistry, 55, 212-209.
[18]      Cox, D. (1995). Water Quality: pH and Alkalinity. University of Massachusetts Extension, Department of Plant and Soil Science, Massa.
[19]      Pal, O. K. (2022). The Quality of Drinkable Water using Machine Learning Techniques. International Journal of Advanced Engineering Research and Science, 9 (6), 16-23.
[20] Song, Y. Y., & Ying, L. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130-135.
[21]      Nick, T. G., & Campbell, K. M. (2007). Logistic regression, Topics in biostatistics. 273-301.
[22]      Leung K. M. (2007). Naive Bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, 123-156.
[23]      Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification Thinking with Examples for Effective Learning. Springer New York, NY.
[24]      Devetyarov, D., & Nouretdinov, I. (2010). Prediction with Confidence Based on a Random Forest Classifier. Artificial Intelligence Applications and Innovations, 44-37.
Iranian Chemical Engineering Journal
Volume 23, Issue 135
September and October 2024
Pages 90-101