Optimization of dual transesterification of jatropha seed oil to biolubricant using hybridized response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS)-genetic algorithm (GA)

• Published in: Sustainable Chemistry for the Environment 2023-12, Vol.4, p.100050, Article 100050
• Main Authors: Ude, Callistus N., Igwilo, Christopher N., Nwosu-Obieogu, Kenechi, Nnaji, Patrick C., Oguanobi, Collins N., Amulu, Ndidi F., Eze, Cordelia Nneka, Omenihu, Uchenna C.
• Format: Article
• Language: English
• Subjects: Adaptive neuro fuzzy inference system; Genetic algorithm; Jatropha seed oil; Response surface methodology; Transesterification
• ISSN: 2949-8392; 2949-8392
• DOI: 10.1016/j.scenv.2023.100050

The over-reliance of the industrial and automobile sectors on petroleum-based lubricants, the feedstocks of which pose environmental challenges, has generated the need for sustainable alternatives in order to promote economic development and a sustainable green environment. The study investigated the optimization of process variables for the dual transesterification of jatropha seed oil into a biolubricant using a hybridized response surface methodology-genetic algorithm (RSM-GA) and an adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA). The seed oil was extracted using a Soxhlet extractor and characterized for its physicochemical properties. The catalyst for the reaction was synthesized by the acid modification of clay. The experimental design was created using Design Expert, and process parameters were optimized using RSM-GA and ANFIS-GA. The yield of oil was 56%, and its properties did not impede the catalyst from transesterification without pretreatment. The modified clay effectively converted the jatropha seed oil into a biolubricant. The ANFIS-GA model attained the highest yields (92.36%) under the optimal parameters of 3 h reaction time, 120 °C reaction temperature, 3% wt catalyst dosage, 5:1 TMP/JSOME molar ratio, and 300 rpm agitation speed. Therefore, the incorporation of ANFIS and RSM with GA was more efficient in optimizing and predicting the biolubricant yield.