article · Sustainable Chemistry for the Environment
This research focuses on producing sustainable biolubricants from jatropha seed oil to serve as alternatives to conventional petroleum-based lubricants. Jatropha oil was obtained through Soxhlet extraction with a yield of 56 per cent. An acid-modified clay was synthesised and successfully used as a catalyst to convert the extracted oil through a dual transesterification process without requiring oil pretreatment. To maximise biolubricant production, researchers optimised the process variables using two computational approaches: response surface methodology combined with a genetic algorithm, and an adaptive neuro-fuzzy inference system combined with a genetic algorithm. The adaptive neuro-fuzzy system linked with the genetic algorithm proved to be the most effective predictive and optimisation method. It achieved a peak biolubricant yield of 92.36 per cent under operating conditions of 120 degrees Celsius, three hours of reaction time, a 3 per cent catalyst dosage, a 5:1 molar ratio, and 300 revolutions per minute.
Industrial and automotive sectors rely heavily on petroleum-derived lubricants, which present persistent environmental hazards. Developing biolubricants from plant sources such as jatropha offers a greener, renewable alternative. Identifying effective catalysts and precise computational models to optimise production parameters helps make the manufacturing of eco-friendly lubricants more efficient and sustainable.
This work demonstrates an applied, laboratory-tested method for manufacturing biolubricants that could interest industrial and automotive lubricant manufacturers seeking bio-based formulations. Utilizing cheap clay catalysts and avoiding oil pretreatment steps may lower processing costs. However, as the research is currently at an experimental, bench-scale optimisation stage, further testing at pilot scale is necessary before direct commercial implementation.
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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 oC 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.
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DOI: 10.1016/j.scenv.2023.100050
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