Response Surface Methodology: A Review on Optimization of Adsorption Studies
Simon Bbumba *
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda, Department of Science, Faculty of Science and Computing, Ndejje University, P.O. Box 7088, Kampala, Uganda and Department of Chemistry, Faculty of Science, Muni University, P.O. Box-725, Arua, Uganda.
Moses Kigozi *
Department of Chemistry, Busitema University, P. O. Box 236, Tororo, Uganda.
Jackline Nabatanzi
Faculty of Business Administration and Management, Ndejje University, P.O. Box 7088, Kampala, Uganda.
Ibrahim Karume
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
Chinaecherem Tochukwu Arum
Department of Material Science and Explosives, Faculty of Science, Nigerian Defence Academy, PMB 2109, Kaduna, Nigeria.
Hussein Kisiki Nsamba
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
Ivan Kiganda
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
Moses Murungi
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
John Ssekatawa
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
Resty Alexandria Nazziwa
Department of Chemistry, College of Natural Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda.
*Author to whom correspondence should be addressed.
Abstract
Herein, we reviewed response surface methodology (RSM), a powerful statistical tool widely used in optimizing adsorption processes to remove synthetic dyes, toxic heavy metals, and phenols from wastewater. The widely used RSM models during optimization are the central composite design and Box-Behnken, which are second-order polynomial models. The models give a predictive insight into the number of experimental runs to be carried out. Furthermore, an in-depth overview of RSM and its application in optimizing various adsorption parameters, such as adsorbent dosage, initial pollutant concentration, contact time, and pH is discussed. In addition, RSM enables researchers to efficiently determine the optimal conditions for maximum pollutant removal. Lastly, the findings of this research highlight the potential of RSM as a valuable tool for optimizing adsorption processes and contributing to sustainable water treatment technologies.
Keywords: Response surface methodology, phenols, toxic heavy metals, wastewater, adsorption