An Experimental and Statistical Approach to Optimize Filtration Rate in a Chemical Process

Industrial processes face increasing pressure to enhance efficiency while maintaining high-quality standards. In the chemical industry, filtration is a critical step that directly influences both the quality of the final product and operational costs. Therefore, optimizing the filtration rate has become a key focus to ensure competitive production. Filtration process efficiency can be influenced by several factors, such as temperature, pressure, and agitation speed. However, the precise impact and interactions of these factors are often poorly understood, resulting in performance variability and costly inefficiencies. Identifying these critical parameters and their interactions is essential to ensure optimal process control. This study aims to identify the key factors affecting filtration rates using a rigorous experimental methodology and quantify their impacts through a detailed statistical analysis, including Analysis of Variance (ANOVA). In particular, a full-factorial experimental design with two levels is employed to study the main effects and interactions of four critical factors, temperature, pressure, formaldehyde concentration, and agitation speed. The experimental results are then analyzed using ANOVA to determine significant contributions. The findings indicate that filtration performance is strongly affected by both individual process parameters and their combined interactions. Experimental results reveal that optimizing temperature and pressure conditions significantly improves filtration efficiency, while appropriate agitation speed contributes to process stability and reduced filtration time. In addition, controlling formaldehyde concentration was observed to have a direct impact on operational consistency and product quality. The optimized parameter combination achieved higher filtration rates together with lower energy consumption and reduced process waste.