Boosting Biodiesel Production from Dairy-Washed Scum Oil Using Beetle Antennae Search Algorithm and Fuzzy Modelling

• Published in: Resources (Basel) Vol. 12; no. 11; p. 131
• Main Authors: Salameh, Tareq, Rezk, Hegazy, Issa, Usama, Kamarudin, Siti Kartom, Abdelkareem, Mohammad Ali, Olabi, Abdul Ghani, Alkasrawi, Malek
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2023
• Subjects: Algorithms; Antennae; beetle antennae search algorithm; biodiesel; Biodiesel fuels; Biofuels; Climate change; Data collection; Decision making; Diesel; Economic growth; Efficiency; Energy; Fossil fuels; Fuzzy algorithms; Fuzzy logic; fuzzy modelling; Fuzzy sets; Fuzzy systems; Linguistics; Mathematical models; Methanol; Milk; Modelling; Navigation behavior; Oils & fats; Optimization; Parameter identification; Process parameters; Production processes; Renewable resources; Scum; Search algorithms; Swarm intelligence; Temperature; Transesterification; Variables; Variance analysis; United Arab Emirates
• ISSN: 2079-9276; 2079-9276
• DOI: 10.3390/resources12110131

The major goal of this study was to develop a robust fuzzy model to mimic the generation of biodiesel from the transesterification of dairy-washed milk scum (DWMS) oil. Four process parameters were considered: the molar ratio of methanol to oil, the concentration of KOH, the reaction temperature, and the reaction time. The proposed technique was divided into two steps: fuzzy modelling and optimum parameter identification. The capability of fuzzy tools to capture and make use of linguistic variables and fuzzy sets is one of their main benefits. This means that fuzzy logic allows for the representation and manipulation of values that fall across a continuum rather than merely relying on crisp values or binary categories. When dealing with non-linear relationships, this is especially helpful since it gives a more accurate and nuanced depiction of the underlying data. As a result, an accurate fuzzy model was initially built based on collected data to simulate the biodiesel production in terms of the molar ratio of methanol to oil, the concentration of KOH, the temperature of the reaction, and the reaction duration. In the second phase, the beetle antennae search (BAS) algorithm was applied to identify the optimal values of the process parameters to boost the production of biodiesel. The BAS algorithm draws inspiration from beetle behavior, particularly how they navigate using their antennae. It employs a swarm-intelligence method by deploying virtual beetles that swarm over the problem area in search of the best solution. One of its main features is the BAS algorithm’s capacity to balance exploration and exploitation. This is accomplished through the algorithm’s adaptable step-size mechanism during the search phase. As a result, the algorithm can first investigate a large portion of the problem space before gradually moving closer to the ideal answer. Compared with ANOVA, and thanks to fuzzy, the RMSE decreased from 7 using ANOVA to 0.73 using fuzzy (a decrease of 89%). The predicted R2 increased from 0.8934 using ANOVA to 0.9614 using fuzzy (an increase of 7.6). Also, the optimisation results confirmed the superiority of the BAS algorithm. Biodiesel production increased from 92% to 98.16%.