Circular Bio-economy of Plantain Waste in the Niger Delta for Optimized Cellulose Recovery and Greenhouse Gas Mitigation
Description
This dataset contains the experimental design matrix and statistical analysis results for optimizing process parameters, generated using Stat-Ease Design-Expert software (version 13). The data was gathered employing a Taguchi L9 (3⁴) orthogonal array, which systematically structured nine experimental runs to evaluate four independent variables across three levels: Temperature (35, 60, and 85 °C), Time (1, 3, and 5 hr), Concentration (2, 3, and 6%), and Mass-to-Solvent Ratio (1:10, 1:15, and 1:20). The primary hypothesis underlying this dataset is that varying key operational parameters (temperature, time, concentration, and mass-to-solvent ratio) significantly impacts the Overall Process Efficiency (OPE). Furthermore, it posits that applying the Taguchi design methodology will identify an optimal parameter combination that maximizes both raw processing yield and systemic stability (robustness against noise). The dataset depicts the quantitative relationship between the extraction parameters and the resulting process efficiency, detailing both raw yield and robustness. It includes comprehensive Analysis of Variance (ANOVA) tables and percentage contribution metrics for both OPE and Signal-to-Noise (S/N) ratios. The ANOVA results identify temperature (F = 291.50, P < 0.001) and concentration (F = 111.95, P < 0.001) as the most highly significant factors, driving 69.88% and 26.09% of the OPE variance, respectively. S/N ratio evaluations reveal that temperature controls 44.00% of the process robustness. Interestingly, the mass-to-solvent ratio emerges as a highly significant secondary factor for maintaining stability, contributing 25.93% to the S/N variance, despite having a low impact on raw yield. Extraction time demonstrated the least influence on the overall operation, contributing only 3.05% to OPE and 7.92% to the S/N ratio. Researchers can interpret this data by examining the F-values, P-values, and percentage contributions to clearly understand the hierarchy of parameter importance. For instance, the high statistical significance of temperature and concentration dictates that these variables require strict monitoring and precise control in practical applications. Conversely, the minimal impact of extraction time suggests that engineers have operational flexibility, allowing for shorter processing times to save energy without severely degrading yield. Future researchers can use this dataset to validate their own predictive optimization models, benchmark comparative studies on biomass and plantain waste valorization, or guide the techno-economic scaling of similar biorefinery processes.
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Steps to reproduce
Post-harvest plantain pseudostems were manually peeled, washed with distilled water, sun-dried for 48 hours, and oven-dried at 60 °C to a constant weight. The fibers were mechanically milled and sieved to isolate a uniform 250–500 µm particle size fraction before being stored in airtight PET containers. All chemical procedures utilized analytical grade Sodium hydroxide (NaOH), glacial acetic acid, and Sodium chlorite (NaClO₂). The extraction apparatus consisted of a three-necked round-bottom flask fitted with a reflux condenser and heated via a digital magnetic stirrer. Following the Taguchi L9 (3⁴) experimental design matrix, exactly 10 g of the prepared biomass was reacted with NaOH. The process was executed according to the specified run combinations of temperature (35–85 °C), extraction time (1–5 h), alkali concentration (2–6%), and mass-to-solvent ratio (1:10–1:20). The resulting mixture was filtered and washed until a neutral pH was achieved. The recovered residue was bleached using 1.5% (w/v) aqueous NaClO₂ (buffered with acetic acid to pH 4.5) and maintained at 70 °C for 2 hours. The final purified cellulose pulp was washed, dried at 60 °C, and weighed. All extractions were executed in triplicate to calculate the mean Overall Process Efficiency (OPE %). The raw yield data and corresponding Signal-to-Noise (S/N) ratios were processed using Stat-Ease Design-Expert (version 13) software to conduct Analysis of Variance (ANOVA) and quantify the percentage contribution of each parameter.
Institutions
- Federal University OtuokeBayelsa State, Ogbia