Predictive Modeling for Pollutant Removal: Machine Learning Algorithms for Predictive Analysis

Wastewater treatment plays an important role in reducing pollutants in discharges, thereby maintaining the quality of water resources, its conservation and perseverance of the nation’s economy, and also in aquatic environmental protection. This is indeed a challenging process and requires complex processes, viz; sedimentation, biological and chemical processes because it involves the variety of wastewater, the tributaries characteristics, the concentrations of pollutants from sources, and the geospatial and climatic conditions. It is governed by a strong oscillation of physical, chemical, and microbiological parameters. Artificial intelligence (AI) is lately being considered as an important tool in apprehending real-world situations. It has revolutionized the industrial sector such as disease diagnosis, material design, and intelligent robotics. AI-based models, especially machine learning algorithms are equipped with conservation of energy economy, water management, and pollution remediation and have accomplished better than conventional modeling tactics and have been implemented successfully in the stimulation of innovation. Interestingly, a machine learning algorithm is enabled to solve complex non-linear problems and explores wastewater treatment, for example, the evaluation of biological oxygen demand (BOD), chemical oxygen demand (COD), efficiency parameters, contaminants detection, elimination of nitrogen and sulphur owing to automation of facilities that resulted in low- and economical cost operations. This chapter is therefore primarily focused on a detailed discussion of apprehension of the ML algorithms in removing heavy metals for the treatment of wastewater.