The beneficial effects of biofuels as components of the worldwide energy supply are unquantifiable because they have versatile applications. However, an adequate understanding of the chemical properties of typical biomass is an integral aspect of maximizing the energy potentials because it is susceptible to biomass behavior during the conversion process, especially anaerobic digestion. Therefore, this study investigated the physicochemical characteristics of selected lignocellulose biomass, namely; cow dung, mango pulp, andChromolaena odorataof Nigerian origin. The raw biomasses were characterized by proximate, calorific, ultimate, compositional, and microbial (for cow dung only) analyses using ASTM standards and equipment. Raw biomass characterization showed that cow dung, mango pulp, andChromolaena odorata leavesrecorded percentages; fixed carbon, volatile matter, and ash contents in addition to calorific values in the ranges of 6.22–7.25%, 5.02–7.79%, 1.14–1.91,% and 13.77–16.16 MJ/kg, respectively. The ultimate analysis of cow dung, mango pulp and Chromolaena odorata recorded carbon (43.08, 39.98, 41.69%); hydrogen (7.87, 6.74, 9.86%); nitrogen (1.53, 1.34, 1.51%); sulphur (0.46, 0.12, 0.25%) and oxygen (47.06, 51.82, 46.69%), respectively. Compositional analysis of the biomass gave percentages in the range of 7.47–11.37 for hemicellulose, 0.22–6.33 for lignin, and 3.71–12.03 for cellulose, while the microbial analysis of cow dung gave total bacteria counts of 5.78 × 10⁸and 3.93 × 10⁵ cfu/g on wet and dry bases, respectively, which implied that it was rich in microbial colonies, evidently from the various species found, such asEscherichia coli, Staphylococcus aureus, Bacillus cereus, Pseudomonas aureginosa, Proteus morganii, and Micrococcusspp. In this regard, the physicochemical properties of selected biomass of Nigerian origin were established to conform with those of the literature and thus can be regarded as suitable feedstock for anaerobic digestion resulting in methane-rich biogas products.

Energy is an essential bedrock, which plays a high impact role in the running of domestic and industrial activities. Most energy used for these activities is majorly from conventional sources, which after combustion result in ecological imbalance, climatic affray, health hazards, and degradation of natural resources. Therefore, the quest for eco-friendly energy has made researchers to investigate on alternative energy, such as biogas. This review study presents a comprehensive analysis of various biomass used for biogas production considering the effects that co-digestion of these materials has on biogas yield, as well as the technology involved. It further evaluated the applicability of artificial intelligence for modeling and optimization of the anaerobic digestion process including the blend ratios, process parameters and so on. These indices determine the percentage methane yield from biomaterial. The review effort revealed that methane content of biomaterials digested without pre-treatment varies from 3.6 ± 0.7 to 443.55 ± 13.68 $NLk{g}^{-1}VS$ while the yield from biomaterials pre-treated using various methods varies from 301.38 mL $C{H}_4$/g $V{S}_{added}$ to 0.73–5.87 L/week. Anaerobic digestion of the blends of cow dung, mango pulp, and Chromolaena odorata was reportedly necessary, as information is scantily available on it. The modeling of the resulting experimental data using different machine learning techniques such as an adaptive-neuro-fuzzy inference system and ANFIS for predicting biogas yield is a major information gathered in this study. The AI models reviewed have high correlation factors ranging from 0.8700 to 0.9998. This information gathered in this paper will motivate the production of useful fuel to complement the existing energy sources while offering a near-term and practical means for reduction of environmental pollution.

One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40days), the agglomerate that comprised 50 cow dung, 25 mango pulp, and 25 Chromolaena odorata produced the highest volume of biogas, 4750m³/kg, while the one with 50 cow dung, 12.5 mango pulp, and 37.5 Chromolaena odorata produced the lowest volume of biogas, 630m³/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m²) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R² values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R² values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78 of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.

The potentiodynamic polarization of aluminium 6063 alloy reinforced with silicon carbide (SiC) and palm kernel shell ash (PKSA) particulates at various mixing ratios were investigated. Double stir casting method was adopted for the production of the hybrid reinforced composites. The existence of the reinforcements within the matrix alloy acted as active sites for corrosion initiation. Hence, pitting corrosion was observed. The range of values for Ecorr and Icorr obtained at 24 h in 1.0 M H2SO4 were between -627.74 and -644.46 mV, and between 423.81 and 860.23 µA/cm2, respectively. The Ecorr values ranged from -654 to -697.22 mV, and the Icorr ranged from 1075.65 to 3057.16 µA/cm2 at 72 h in 1.0 M H2SO4. The relative resistance to corrosion of the samples is dependent on the thin oxide film formed on the surface of the samples.

Charcoal is a popular form of biofuel embraced for domestic and industrial purposes. However, the use of Charcoal has some associated challenges, such as the required charcoal pot and setting it into the fire at first by using Charcoal-Ignition-Aiders (CIA) (e.g. discarded paper, nylon, rubber, plastics, petrol, the residue of processed palm oil, maise cob, wood, and kerosene). Coupled with the chemical properties of Charcoal, the resulting gases from CIA are capable of polluting the environment with perceived Adverse-Health-Implications (AHI) on the ecosystem. Therefore, this study conducted a safety assessment of charcoal biofuel usage and the effects of common CIA on combustion indices. This study followed standard methods and the use of peculiar equipment. This study established that Charcoal is commonly used in the studied area because it is cheap, readily available and requires less technical know-how. Considering the combustion indices, using paper as a CIA generated the lowest carbon monoxide (CO) value, 28.1 ppm, with 3,434.54 ppm volatile organic compound, VOC. Compared with the ACGIH standard permissible exposure level of ≤ 30 ppm, the paper gave a lesser CO value of 28.10 ppm among all the CIA. At the same time, all the CIA recorded higher VOC compared with EPA standard permissible exposure level of ≤ 15 ppm. ANOVA analysis conducted on the socio-demographic profile of the respondents, cooking attributes of the respondents, and use of charcoal pot types by the respondents in Zone 1, Zone 2, and Zone 3 gave p-values of 0.032, 0.028, and 0.039, respectively. These imply significant differences within the zones in each of the indices. The average energy content reported for charcoals sourced from oak trees, afara, obeche, mahogany, and iroko woods is 3,2149 kJ/kg compared to the lower ones. Therefore, this study recommended using these charcoals alongside discarded paper as CIA because they are a better combination to reduce AHI.