Data on fuzzy logic based-modelling and optimization of recovered lipid from microalgae.

Area of Science:

• Biotechnology
• Chemical Engineering
• Data Science

Background:

• Microalgae are a promising source for biofuel production, with lipid extraction efficiency being a key challenge.
• Optimization techniques are crucial for maximizing lipid yield from microalgal biomass.
• Previous work established a fuzzy model and PSO for enhancing lipid extraction.

Purpose of the Study:

• To present and structure data related to microalgae lipid recovery.
• To provide reference datasets for evaluating optimization techniques in lipid extraction.
• To detail the performance of fuzzy logic modeling and PSO in this context.

Main Methods:

• Fuzzy logic-based modeling for lipid recovery prediction.
• Analysis of variance (ANOVA) for statistical validation.
• Particle Swarm Optimization (PSO) algorithm for process optimization.
• Data presentation of recovered lipid percentages and PSO cost function variations.

Main Results:

• The study presents data on the percentage of recovered lipid using fuzzy logic and ANOVA.
• It details the variation of the cost function (recovered lipid) across 100 runs of the PSO algorithm.
• These datasets serve as benchmarks for other optimization methods.

Conclusions:

• The presented data offer a valuable resource for researchers in microalgae lipid extraction.
• The findings support the utility of fuzzy logic and PSO in optimizing biofuel precursor recovery.
• The datasets can facilitate comparative analysis of different optimization strategies.