The review article "Response Surface Methodology for Process Optimization in Livestock Wastewater Treatment: A Review" by Arif Reza, Lide Chen, and Xinwei Mao, published in Helio Journal, discusses the application of Response Surface Methodology (RSM) in optimizing livestock wastewater (LWW) treatment processes. The authors highlight the environmental and social challenges posed by LWW, emphasizing the need for effective treatment methods to minimize environmental impact and ensure compliance with regulatory standards. RSM is introduced as a robust optimization technique that can improve the efficiency and sustainability of LWW treatment systems. The article outlines the key steps of RSM, including experimental design, data collection, response surface modeling, model evaluation, model interpretation, process optimization, and validation. It also reviews recent applications of RSM in various LWW treatment processes, such as biological, physical, and chemical treatments. Biological processes like anaerobic digestion and co-digestion are optimized to enhance biogas production and nutrient removal. Physical processes, including air stripping and vacuum thermal stripping, are optimized to improve pollutant removal efficiencies. Chemical processes, such as precipitation, electrocoagulation, and adsorption, are optimized to achieve better treatment performance and resource conservation. The authors identify several challenges associated with RSM application in LWW treatment, including simplified modeling assumptions, variability in real-world conditions, model validity and accuracy, data availability and quality, robustness and generalization, and the lack of consideration for techno-economic factors. They propose potential improvements, such as incorporating advanced modeling techniques, dynamic RSM, expanding experimental design space, integrating multi-objective optimization, conducting sensitivity analysis, integrating techno-economic analysis, considering environmental and social factors, and integrating real-time monitoring and control. Future research directions include combining RSM with other modeling techniques, developing integrated LWW treatment processes, exploring innovative technologies for emerging contaminants, incorporating uncertainty quantification and risk analysis, integrating decision support systems, considering social and cultural aspects, adapting RSM to different livestock waste streams and regions, and integrating life cycle assessment and economic analysis. In conclusion, RSM has shown great potential in optimizing LWW treatment processes, but further research is needed to address its limitations and enhance its applicability in real-world scenarios.The review article "Response Surface Methodology for Process Optimization in Livestock Wastewater Treatment: A Review" by Arif Reza, Lide Chen, and Xinwei Mao, published in Helio Journal, discusses the application of Response Surface Methodology (RSM) in optimizing livestock wastewater (LWW) treatment processes. The authors highlight the environmental and social challenges posed by LWW, emphasizing the need for effective treatment methods to minimize environmental impact and ensure compliance with regulatory standards. RSM is introduced as a robust optimization technique that can improve the efficiency and sustainability of LWW treatment systems. The article outlines the key steps of RSM, including experimental design, data collection, response surface modeling, model evaluation, model interpretation, process optimization, and validation. It also reviews recent applications of RSM in various LWW treatment processes, such as biological, physical, and chemical treatments. Biological processes like anaerobic digestion and co-digestion are optimized to enhance biogas production and nutrient removal. Physical processes, including air stripping and vacuum thermal stripping, are optimized to improve pollutant removal efficiencies. Chemical processes, such as precipitation, electrocoagulation, and adsorption, are optimized to achieve better treatment performance and resource conservation. The authors identify several challenges associated with RSM application in LWW treatment, including simplified modeling assumptions, variability in real-world conditions, model validity and accuracy, data availability and quality, robustness and generalization, and the lack of consideration for techno-economic factors. They propose potential improvements, such as incorporating advanced modeling techniques, dynamic RSM, expanding experimental design space, integrating multi-objective optimization, conducting sensitivity analysis, integrating techno-economic analysis, considering environmental and social factors, and integrating real-time monitoring and control. Future research directions include combining RSM with other modeling techniques, developing integrated LWW treatment processes, exploring innovative technologies for emerging contaminants, incorporating uncertainty quantification and risk analysis, integrating decision support systems, considering social and cultural aspects, adapting RSM to different livestock waste streams and regions, and integrating life cycle assessment and economic analysis. In conclusion, RSM has shown great potential in optimizing LWW treatment processes, but further research is needed to address its limitations and enhance its applicability in real-world scenarios.