A Soft Computing Approach to Optimize the Clarification Process in Wastewater Treatment

The coagulation process allows for the removal of colloidal particles suspended in wastewater. Estimating the amount of coagulant required to effectively remove these colloidal particles is usually determined experimentally by the jar test. The configuration of this test is often performed in an iterative manner which has the disadvantage of requiring a significant period of experimentation and an excessive amount of coagulant consumption. This study proposes a methodology to determine the optimum natural coagulant dose while at the same time eliminating the maximum amount of colloidal particles suspended in the wastewater. An estimation of the amount of colloidal particles removed from the wastewater is determined by the turbidity in a standardized jar test, which is applied to the wastewater at the wastewater treatment plant in Logroño (Spain). The methodology proposed is based on the combined use of soft computing techniques and evolutionary techniques based on Genetic Algorithms (GA). Firstly, a group of regression models based on neural networks techniques was performed to predict the final turbidity of a wastewater sample taking into consideration a configuration of jar test inputs. The jar test inputs are: initial turbidity, natural coagulant dosage, temperature, mix speed and mix time. Finally, the best combination of jar test inputs to obtain the optimum natural coagulant dose, while also eliminating the maximum amount of colloidal particles, was achieved by applying evolutionary optimization techniques to the most accurate regression models obtained beforehand.