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In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.

The use of microalgae for CO2 sequestration helps in mitigating global warming. This paper reviews the application of microalgae for CO2 sequestration with emphasis on performance evaluation, lifecycle, economic assessment, as well as environmental impact. The CO2 sequestration mechanism is done during photosynthesis, via bioconcentration. Performance evaluation revealed that the efficiency of capture and sequestration of CO2 by microalgae ranges between 40% and 93.7%. However, the macro performance of microalgae in carbon emission reduction has not yet been fully understood, and therefore requires more studies. Also, the cost-effectiveness of the CO2 sequestration by microalgae cultivation still needs more research based on recent economic realities. It is recommended that design and operation be focused on cost-effectiveness so the technology can compete favourably with existing technologies. Overall, it can be surmised that the application of microalgae for CO2 sequestration is an effective technique for carbon capture, but still has interesting areas for development for greater global environmental impact.

The increase in the demand for a reliable electricity supply by the utilities and consumers has necessitated the evaluation of the reliability of power systems. A reliable electricity supply is characterized by no or minimal duration and frequency of supply outages. This has triggered the necessity of using renewable energy sources (RESs) with optimization methods for reliability improvement of electricity systems and reduction of greenhouse gas (GHG) emissions. The main objective of this study is to optimize micro-grid systems operations, improve reliability, reduce emissions and balance the demand and supply of energy through RESs and battery energy storage system (BESS). The adaptive model predictive control (AMPC) method is used to address the issues of micro-grid operation. The AMPC algorithm solves the optimization problem of disturbance prediction in a micro-grid with different types of RESs and BESS integration. This optimization problem considers different constraints for minimum operating costs in different case scenarios. The financial viability of the proposed method is investigated. Solar photovoltaic (Solar PV), wind plant (WP) and BESSs are used with the AMPC method to investigate the impacts of annual real interest rates on cost and emission parameters, quantification of the emission oxides from different case scenarios, reduction of the cost of electricity (Ccoe), and power system reliability improvement. A modified Roy Billinton Test System (RBTS) is used to confirm the reliability enhancement and financial feasibility of the system. Case studies are used to confirm the proposed methods using climatic data for the city of Pietermaritzburg (29.37° S and 30.23° E), South Africa. The results obtained establish that the incorporation of RESs and BESSs using the AMPC method gives satisfactory outcomes.

Several studies have investigated the perception and awareness level of renewable energy among various groups of people around the world. Not much is known about the perception and knowledge level of undergraduate engineering students in Nigeria towards renewable energy, even though her engineering students have a crucial role to play in ushering the country into the net-zero carbon economy of the future. This research was carried out to provide answers to questions concerning the perception and awareness level of engineering students in Nigeria towards renewable energy. Quantitative data obtained via a questionnaire survey showed that 98.1% of the engineering students agreed to have heard about renewable energy, while only 24% are highly confident about their understanding of renewable energy. 76% agreed that renewable energy is not part of their undergraduate curriculum, and 90.1% want renewable energy to be included in their curriculum. From the findings it was observed that there is an equal understanding of renewable energy among male and female engineering students in Nigerian universities. The challenges associated with introducing renewable energy into undergraduate engineering programs and the strategies for deploying renewable energy into the undergraduate curriculum for engineering students were discussed. A new curriculum with renewable energy education was proposed. This study would be beneficial to the Nigerian government and relevant university regulatory bodies in Nigeria, such as the Nigerian Universities Commission (NUC), and the Council of Registered Engineers in Nigeria (COREN), in making decisions as regards engineering students in Nigeria and in drafting renewable energy policies.

As a result of the damaging effects they have on the climate, recent campaigns against fossil fuels, constantly emphasize the need to completely eliminate their use. To this effect, is a pressing need to source for other sources of energy that have the ability to decrease the level of greenhouse gas emissions to the atmosphere. These alternatives have to be easily accessible and should contribute to the industrial expansion of developing regions, especially Sub-saharan Africa. Poor access to electricity is a major challenge that contributes to the retarded state of development of some countries in Sub-saharan Africa, and renewable energy alternatives, such as biofuels, can perfectly solve this oddity. Biofuels possess unique attributes that can help the world attain a state of energy security and energy balance. The depletion of fossil fuels and other factors like technological advancement and current industrial expansion occurring in most countries in Sub-saharan Africa poses a threat to the availability of energy in the region and it is a major setback to the achievement of the United Nation's Sustainable Development Goal 7. Narrowing down to the use of biodiesels, implementing their use can save the continent from a lot of damage and build up a more sustainable energy sector. Massive deployment of Biodiesels into the energy sector of Sub-saharan Africa will not only ease the difficulty in accessing energy, it will also boost the agricultural sector and economy of the countries that constitute the region. This paper reviews the current state of biodiesel in the African sub-saharan region.

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.