Application of Neurogenetic Modeling in Optimization of Water Treatment Plant Based on the Temporal Monitoring of Water Input Quality

Introduction

Surface Water Treatment Plants (SWTP) are an integral part of the socio-economical development of a region. They play a vital role in maintaining public health and sanitation at the required levels and also support industries varying from agriculture to service provision. In effect water treatment plants are responsible for maintaining the basic requirement of life and health security of the society.

A major challenge that SWTPs face is the water quality dynamic that exists within the different phases of water treatment. The changes in concentration of different quality parameters often require engineers to adjust the dozing requirements. If a change in the incoming water quality remain unnoticed then possibility of health hazards in the supplied region is hard to avoid.

Naturally found water is frequently not pure water and contains many dissolved and suspended impurities, derived from the atmosphere, from catchment areas as well as from the soil. Hydrogen Sulphide, oxygen, ammonia, sodium and calcium carbonates (dissolved impurities) as well as clay, silt, sand, mud and microscopic plants (suspended) are some of the commonly found impurities of water. The most hazardous water pollution is caused by urbanization and industrialization. Industrial waste contains toxic agents ranging from metallic salts to complex synthetic organic chemicals. Whenever the water is polluted by these things, it creates serious health complications to animals, plants and human beings living in surrounding areas. In addition to changes in industrial patterns this temporal variation in the form of the monsoon also effects water quality by changing the volume of water in input sources. Daily variation in water quality is also seen owing to domestic consumption patterns.