Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. As a result, beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. ANN bioprocess development and optimization of umqombothi were done using RSM and ANN. The optimum condition values were 1.1 h, 29.3 °C, and 25.9 h for cooking time, fermentation temperature, and fermentation time, respectively. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R²) closer to 1. RSM significant parameters: alcohol content, total soluble solids (TSS), and pH had R² values of 0.94, 0.93, and 0.99 respectively while the constructed ANN significant parameters: alcohol content, TSS, and viscosity had R² values of 0.96, 0.96, and 0.92 respectively. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools.

This study explores the potential of converting waste tires, low-density polyethylene (LDPE) waste plastic, biomass (sawdust), and their blends into pyro-oils. Comprehensive analyses, including proximate and ultimate analyses and calorific value (CV) assessments, were conducted to determine the elemental composition, moisture content, ash content, fixed carbon, volatile matter, and energy density of the feedstocks. Thermogravimetric analyses (TG and DTG) assessed the thermal stability and decomposition points, while scanning electron microscopy (SEM) examined the surface characteristics and morphology of the feedstocks and their blends. These characterizations were crucial for evaluating the suitability of the feedstocks for pyrolysis. DTG analysis indicated decomposition temperature ranges of 315-498 °C for waste tires, 420-534 °C for waste plastic, 224-413 °C for biomass, and 220-527 °C for the blend. FTIR analysis of the produced pyro-oils identified functional groups such as phenols, alcohols, ketones, aldehydes, and alkanes. The study also investigated the effects of feedstock composition and temperature on the synergistic effect of pyro-oil yield. The highest CVs of pyro-oil were 50.28 MJ/kg from waste plastics, 47.36 MJ/kg from waste tires, and 43.39 MJ/kg from the blends at a 2:1:1 mass ratio at 550 °C. The highest pyro-oil yields were wt % from waste tires with 46.7 and 53.3 wt % at 500 and 550 °C, respectively, and 41.7 wt % from the blends at a 2:1:1 mass ratio at 550 °C. Optimal operating conditions were determined to be 550 °C with a 2:1:1 mass ratio for maximizing pyro-oil yield. Overall, this study provides valuable insights into the potential of combining waste tires, waste plastics, and biomass for pyro-oil production, emphasizing the importance of specific operating conditions and feedstock ratios to achieve optimal results.

One of the issues associated with the supply of electricity is its generation capacity, and this has led to prevalent power cuts and high costs of usage experienced in many developing nations, including South Africa. Historical research has shown that the annual rate of increase for electricity has grown at an alarming rate since 2008 and, in some years, has grown as much as 16%. The objectives of this study are to estimate the cost analysis of electricity usage at the twenty-nine residences of the University of Johannesburg (UJ-Res) and propose a model for our university, as well as other South African universities, to become more energy-efficient. This was achieved by analyzing the tariffs between 2015 and 2021. A forecast was made for a period of five years (2021 to 2026) using a non-linear autoregressive exogenous neural network (NARX-NN) time-series model. From the results obtained, the better NARX-NN model studied has a root mean squared error (RMSE) of 2.47 × 10⁵ and a determination coefficient ($R^2$) of 0.9661. The projection result also shows that the annual cost of energy consumed will increase for the projected years, with the year 2022 being the peak with an estimated annual cost of over ZAR 30 million (USD 2,076,268).

Prior to this study, the thermal stability of fly ash-based geopolymer (FAG) construction materials as a function of cellulose nanocrystal (CNC) concentration and the optimal CNC dosage leading to thermal stability have not been investigated. This study investigates the influence of CNC reinforcement and curing duration on the thermal stability of FAG geopolymer. A series of samples incorporating CNC dosages ranging from 0 to 1.86 wt.% were prepared and subjected to 24- and 48-h curing regimes to evaluate their thermal degradation behaviors. Thermogravimetric analysis (TGA) revealed that 24-h-cured samples exhibited steeper weight losses compared to 48-h-cured ones, particularly in the 100–600 ℃ range. This trend was attributed to incomplete stabilization of organics in shorter curing times. Among all dosages, the 48-h-cured 1.7 wt.% CNC sample demonstrated the lowest total weight loss (~ 9.6% lower than the control), indicating enhanced thermal resistance. Derivative weight analysis further confirmed this, showing the lowest peak weight change rate (0.105%/℃) for the 1.7 wt.% CNC sample cured for 48 h, compared to 0.304%/℃ for the unreinforced control. Additionally, differential scanning calorimetry (DSC) indicated reduced exothermic heat flow in 48-h-cured samples, especially in the 1.7 wt.% CNC formulation, suggesting minimal phase transitions and improved thermal reliability. The novelty of this work lies in demonstrating the synergistic enhancement of thermal resistance through CNC addition and extended curing. Unlike prior studies that primarily focused on mechanical reinforcement, this research establishes an optimal CNC dosage (1.7 wt%) that minimizes thermal degradation, offering critical insights for thermally stable, bio-reinforced geopolymer development. These findings support the application of CNC–geopolymer composites in fire-resistant, sustainable construction materials. Graphical Abstract

Nanomaterials (NMTs) production modeling and optimization is a huge task. Even though several authors have been able to model the process, nevertheless, deeper investigation needs to be carried out since the process itself is a combination of several disciplines from physical sciences to engineering. This chapter investigates various research works that have carefully modeled the production process of carbon-based NMTs (mainly carbon nanotubes) over the last two decades. Models such as the machine-learning models, simple weight model, kinetic model, as well as the model derived from chemical kinetic models, are all investigated and presented in this chapter. In addition, reactors such as fixed bed reactors, fluidized bed reactors, screw kilns, and autoclaves have all been employed in NMT production using different techniques. The optimization of NMT production process is carefully evaluated from the works of authors who have found ways to improvise NMT production from a combination of different processes, pre-processes, catalysts, operating conditions, and reactors, amongst several others. To optimize the NMT production process aside from the conditions and variables mentioned, the coproduction of valuable products such as hydrogen gas is essential. <!----> <!----> <!----> <!---->

This chapter explores the pivotal role of biomass feedstock in the sustainable production of biochemicals, bioplastics, and biobased materials. As global industries shift away from fossil-derived inputs, biomass offers a renewable, carbon-neutral alternative for manufacturing high-value products. This chapter categorizes biomass sources into lignocellulosic residues, agricultural byproducts, food waste, and dedicated energy crops, highlighting their chemical composition, availability, and conversion potential. Key thermochemical and biochemical pathways, such as fermentation, pyrolysis, and hydrolysis, are discussed in relation to their efficiency, scalability, and environmental impact. Special attention is given to the integration of biomass into circular bioeconomy models, where waste valorization and resource recovery are prioritized. Case studies illustrate successful applications in packaging, construction, and pharmaceuticals, demonstrating the versatility of biobased materials. Challenges such as feedstock variability, pretreatment costs, and policy gaps are also addressed, alongside emerging innovations in biorefinery design and green chemistry. Ultimately, this chapter underscores biomass feedstock as a cornerstone for advancing low-carbon manufacturing and fostering sustainable industrial transformation.