Coagulation and Flocculation Optimization for Sustainable Wastewater Treatment Investigation of coagulation methods and predictive modelling to reduce chemical consumption and carbon footprint

Abstract

This thesis investigates opportunities for optimizing the primary coagulation and flocculation process at the company that treats industrial wastewater. The treatment process handles highly variable incoming raw water, where chemical dosing is currently based on fixed dosages. These variations in incoming water quality create opportunities to proactively improve resource efficiency and process stability, with a primary focus on reducing environmental impact. The aim of this work was to investigate how variations in raw water quality influence the efficiency of the coagulation and flocculation process and to examine how a data driven approach can support and improve chemical dosing. The objective was to reduce chemical consumption and its associated carbon footprint while maintaining or improving treatment performance. This thesis was based on laboratory jar tests using wastewater collected from the inlet of the treatment process following initial sedimentation. A range of experimental conditions were tested and evaluated, including the current performance of TOC, absorbance and turbidity removal, pH adjustments for ferric chloride, polymer addition, mixing strategies and dilution series. The dilution series was evaluated based on the measurement error between the instruments used at Chalmers and at the company's lab. Key water quality and performance parameters for the coagulation and flocculation process were measured, including TOC, turbidity, absorbance, conductivity and sludge production. In addition, a linear model was developed in Python to predict TOC removal from SUVA. Based on this model, an algorithm was proposed to predict the coagulation outcome. The algorithm suggests an optimal chemical dose based on these raw water quality parameters. The results show that the performance of the treatment process is influenced by several operational conditions, such as pH and mixing strategies, and by how these are managed in relation to variations in the incoming raw water quality, which directly affects the efficiency of contaminant removal. The study also demonstrates that the characteristics of the raw water influence the required treatment level, highlighting the importance of an adaptive and flexible approach to chemical dosing. Furthermore, the findings indicate that transitioning from a fixed dosing strategy to a more adaptive approach can improve both plant stability and chemical use efficiency, resulting in reduced carbon footprint. By combining experimental and modelling approaches, this thesis demonstrates how treatment performance can be better understood and managed under varying operating conditions.