Sphesihle Mtsweni

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends.

Lignocellulosic biomass, derived from plant materials, represents a renewable alternative to fossil fuels and plays a crucial role in advancing environmental sustainability. This systematic review investigates recent developments in the conversion of lignocellulosic biomass into biofuels, with a focus on pre-treatment technologies that enhance enzymatic hydrolysis, a critical step in efficient biofuel production. This review addresses two primary questions: (1) What are the most effective pre-treatment methods for enhancing enzymatic hydrolysis in lignocellulosic biomass conversion? (2) How do these pre-treatment methods compare in terms of efficiency, environmental impact, and economic feasibility? Consequently, studies were selected based on inclusion criteria that focus on research investigating these pre-treatment methods and their comparative performance. A structured search of original studies was applied across databases such as Crossref, Google Scholar, Scopus, PubMed, and Semantic Scholar, resulting in the inclusion of 17 peer-reviewed articles published between 2019 and 2024. The findings highlight effective pre-treatment methods that significantly improve enzymatic accessibility and bioethanol yields. However, ongoing challenges such as feedstock variability, process efficiency, and cost-effectiveness remain. These results highlight the need for further research and development to optimize conversion technologies and identify new areas for exploration.

In the last few years, the mining and metallurgy industry has made concerted efforts to improve waste management through a byproduct recovery strategy, mainly focusing on developing innovative technologies to provide sustainable solutions. This strategy has seen the metallurgy industry exploit more natural resources in waste streams while reducing its environmental impact, making the ‘zero-waste’ goal possible. As such, the concept of circular economy emerged, which seeks to improve the environmental sustainability of mining operations by recycling and reusing the generated waste as raw materials for producing other new products. This paper aims to analyze the findings from published studies on the treatment and stabilization technologies of metallurgical waste or byproducts for the construction industry. Furthermore, the paper synthesizes information on processes and treatment strategies to beneficiate the waste materials for application in the building and construction sector. Finally, the paper identifies knowledge gaps in the literature, using a comprehensive overview of the superior results achieved by the metallurgical industry and potential synergies with other industrial sectors. In conclusion, the paper presents future opportunities while highlighting specific areas that may be further explored. This review paper is helpful to researchers in the mining waste management discipline to have an aerial view of what has already been achieved in the field to improve the existing processes for environment preservation.

This study seeks to investigate how chemical engineering students from South African low-income communities locate knowledge structures. The study used an existing Engineering Related Beliefs Questionnaire (ERBQ) to evaluate beliefs of 268 chemical engineering students. The questionnaire collects additional information by allowing open-ended responses on each item to increase reliability of the questionnaire. Findings suggest that more than 60 per cent of students believe that engineering knowledge cannot be argued, and that learning takes place only when the lecturer transmits knowledge. Engineering educators may consider a humanizing pedagogy, which create opportunities for students from low-income communities to be liberated and reduce the dependency culture. Application of this pedagogy may assist students to achieve life long learning whilst developing necessary soft skills like independent thinking and innovation.