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Background: Several aquaculture industries in underdeveloped nations use fossil fuel-powered generators to produce electricity. This pattern has raised greenhouse gas emissions as well as the price of aquaculture products. Methods: To address this issue, this study contains a bi-objective model that optimizes the parametric settings of waste-to-energy (WTE) plants for aquaculture firms: Levelized cost of energy and power expenses for reverse logistics. The best values for these objectives were created using a genetic algorithm and goal programming. Results: Four planning periods were taken into account during implementation, and actual data were gathered from a Nigerian aquaculture company. The electricity costs from biodiesel ranged from N0.7541 per kW to N0.7628 per kW, respectively. Reverse logistics has energy costs ranging from N6 329 492.10 to N7 121 015.53. The proposed model produced average values for several WTE parametric parameters, including a 1.69 million kg hydrogen gas, a 59.16% hydrogen gas compression efficiency, and an 83.39% electricity conversion efficiency. Furthermore, the system had logistics’ minimum and maximum fractions of 0.18% and 21%, respectively. Conclusion: Our findings demonstrated how WTE parametric parameters impact the aquaculture industry’s electrical power unit.

AbstractTo improve the electricity services in Nigeria, the government has embarked on a total reform of the Nigerian Electricity Supply Industry. The reform started with rehabilitating the government-owned electricity infrastructures in 1999 and implementing the 2010 Power Sector Reform. While some stakeholders have seen these reforms benefit the industry, others have yet to see the positive impact of the reforms. Based on this premise, this work presents a synopsis of the Nigerian power sector’s past, present, and future. A review of its state of the art is explored and presented using documents and recent literature on the Nigerian electricity sector. Findings from the study show that infrastructural deficits and administrative lapses dominated the pre-liberation era. The privatization of electricity led to organizational structure and infrastructure improvements. The sector was unbundled into the GenCos, TransCo, Discos, and the regulatory bodies assigned well-defined tasks. The generation capacity has increased to 16,384 MW against the pre-liberation figure of approximately 6000 MW. As against the approximate figure of 10,000 km covered by the transmission infrastructure, an additional 10,000 km has been added to the existing transmission facilities. Although there have been improvements in service deliveries, there are still more grounds to cover to stabilize the Nigerian electricity sector. It is proposed that stakeholders harmonize the various policies and structural changes to make the necessary improvements.

This present paper is an investigation of a framework for Safety-Critical Maritime Infrastructure (SCMI) evaluation. The framework contains three Multi-Criteria Decision-Making (MCDM) tools, namely: fuzzy Step-wise Weight Assessment Ratio Analysis (SWARA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregates Sum Product Assessment (WASPAS). It also contains five safety practice criteria: people's safety, property safety and monitoring capabilities, response to regular and irregular threats in a robust yet flexible manner, and breaches in physical security. The framework has four safety culture criteria: learning from experience and inter-element collaboration, lack of facility maintenance, and anticipating risk events and opportunities. Through the framework, an evaluation of the safety practices and safety culture of six Nigerian seaports is done. Then, data obtained from the ports in regard to their safety practices and culture were analysed in line with the framework identified and adopted. The results revealed that the safety of people's life is the most important safety practice and contributed about 47.90% to evaluate the SCMI. Results equally showed that the most significant safety culture is learning from experience, and accounted for approximately 53.20% in assessing the SCMI. Similarly, the TOPSIS method ranked Warri (A5) and Tin Can Island A1 as the best and worst safety practices performance, respectively. Results of analyses on the TOPSIS showed that Apapa port (A2) and Onne port (A6) had the best and worst performances correspondingly. WASPAS was also analysed. The results indicated that A6 performed the best safety practice performance, while A2 had the worst safety practice performance. Analysis of the WASPAS method was equally done. It showed that A1 had the worst safety culture performance, while A5 had the best safety culture performance. Therefore, the proposed framework could serve as a veritable tool for analysing SCMI by using safety practice and safety culture criteria.

Business systems will experience new data-driven models for their performance evaluation in the coming years, especially systems with stochastic characteristics. This development will benefit experts in energy management because more problems will be solved using machine learning algorithms - such as artificial neural networks (ANN). This research develops a machine-learning model for electricity sales using a single hidden layer ANN model. The developed model consists of six input parameters, including the number of renewable energy systems and households. This research used principal component analysis (PCA) algorithm to reduce the inputs to three parameters to improve the model performance. A TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method was used to select the most suitable predictive models between SVR (support vector regression) and ANN. Data sets from a community in Lagos, Nigeria, were used to test the developed model performance. This research observed that a SVR model with a linear function performed better than an SVR model with a radial basis function or polynomial kernel. On the other hand, an ANN with 15 neurons outperformed ANN models with fewer nodes. The selected ANN model training and testing mean square errors are 0.00007 and 0.00028, respectively. This research recommends PCA for input parameters selection during electricity sales prediction based on the developed sales model performance.

Electricity plan for a low-cost housing estate in developing countries requires a comprehensive understanding of relationships between energy resources and household’s electricity budgets. In this article, an optimization model is proposed for an electricity plan in low-cost housing estate during a total lockdown. The four decision variables contained in proposed optimization are generators operation time, energy storage systems (ESS) operation time, generator noise and electricity supply from national a grid. Particle swarm optimization algorithm was selected as a solution method for the model. The proposed model was used to optimize electricity consumption for 30days lockdown in three low-case housing estates in Lagos, Nigeria. Based on the optimal results, the cost for generator usage is N 64,399,914.12, while the cost for the ESS usage is N310, 072.77. This article’s concluding remarks highlights its contributions to energy literature. The average daily noise dosage for estates 1, 2 and 3 are 69.17, 69.30 and 69.21db, respectively. The average daily CO2 emitted for estates 1, 2 and 3 are 41.52, 43.60 and 41.43kg, respectively.

At present, labour unions of waste disposal agencies and company management are at loggerheads, frequently turning out contradictory sanitation assessments. This reveals a shifting outlook of sanitation accomplishment that should be resolved. Unfortunately, there is scanty research on road sanitation and no study exists on how to determine the important workforce variables of these workers. To solve this research problem, a multi-objective optimisation model is developed and solved using the differential evolution model. The proposed model considered different constraints including workforce size, budgets, and service time. Three conflicting goals of maximization of cleanliness, maximization of workers' effectiveness and minimization of traffic obstruction were incorporated into the model and solved using practical data from a waste disposal agency in a developing country. A key result shows that the system's average workers' turnover rate is 0.2472 while the system's average service failure rate is 0.2518. For each location, the system requires an average of eight workers per period. The worker's average quality of work done is 0.8552. The outcome of the work revealed the feasibility of the model application. It was concluded that the model serves as a basis to evaluate road sanitation workers and may be used for budgetary purposes.

As energy utility firms expand their production outputs from renewable energy resources, interest in investment in an energy storage system (ESS) will increase in the coming years. This article determines the optimal number of hidden nodes and the maximum number of epochs to generate the best prediction results for this generated power estimation. Primary energy efficiency, energy ratio, and ambient air temperature were used to predict the generated power of an ESS. Experimental data sets for compressed air energy storage (CAES) were used to train a single hidden layer artificial neural network (ANN) model. The developed ANN models for CAES and compressed air storage with humidification (CASH) show quick convergence to the targeted error function. Based on eight different artificial neural network architectures, a 3−5−1ANN architecture generated the best results. This architecture mean squared error was 0.00042. Using experimental data for CASH to validate the single hidden layer model, the ANN model performed satisfactorily.

Renewable energy systems (RES) penetration has improved interest in energy storage systems. This has helped to increase RES acceptance because their reliability has improved. Currently, scholars have recognized compressed air energy storage (CAES) systems as efficient storage systems for RES, but there is a need to subject the implications of these systems’ parametric settings on their storage efficiency. Hence, this study proposes and selects a suitable support vector regression model for storage efficiency prediction. It considered five input parameters — among which are Maximum exit temperature and thermal energy. Data sets for CAES with high temperatures (HTE) were used to analyze the performance of different kernels – radial basis function (RBF), polynomial, and linear – for SVR models. These data were divided into train and test examples. The study observed that an RBF trained-SVR model can accurately predict the storage efficiency of CAES-THE, whereas a polynomial trained-SVR cannot predict the storage efficiency of CAES-HTE. The RBF trained-SVR model testing examples correlation coefficient was higher than its training examples result (0.9855) by 1.43%.

This paper proposes a framework for selecting energy storage technology (EST) for small-scale renewable energy systems. The present study was able to achieve this using the results that were generated from a Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and a fuzzy quality function deployment (FQFD) model. Using the FQFD model, experts' opinions were incorporated into the framework during the evaluation of the techno-economic requirements importance for the selection process. The FQFD model considered system safety, availability, flexibility, reliability and effectiveness as customers’ requirements, while nominal voltage, nominal discharge current, peak measured voltage, operating cost and cost of energy were among the techno-economic requirements. During the framework application, a questionnaire was used to collect information from experts on the techno-economic requirements importance, while information on the techno-economic (eleven) requirements for four ESTs were obtained from literature. The FQFD model results were incorporated into a PROMETHEE and TOPSIS models. The PROMETHEE and the TOPSIS models identified Lithium-Ion EST as the best EST for small-scale renewable energy systems.

In recent times, renewable energy-based power systems are being used to address energy poverty or shortage that is experienced in developing countries. To improve these systems’ applicability, they are used to design a hybrid energy system. It is against this backdrop that this paper focuses on how hybrid energy systems can be designed optimally to address electricity sharing between domestic and productive use in remote communities. This paper, therefore, proposes a mixed-integer multi-objective optimization model for electricity sharing between domestic and productive use in remote communities. The model considered the number of solar photovoltaic (PV) systems acquisition, the total cost of energy utilized, and the cost of CO2 emissions avoided. A genetic algorithm was used to optimize these decision variables. The proposed model evaluation was carried out using data from three remote communities in South-West Nigeria. The results obtained from the model show that the number of installed solar PV systems in the first, second, and third communities is 74, 76 and 73 solar PV systems, respectively. For 25 years planning period, the first community required 29,554.05 kWh, the second community required 28,280.20 kWh, and the third community required 28,608.70 kWh. The average values for productive use of electricity from the conventional energy sources for the first community was 0.358, for the second community was 0.338, and for the third community was 0.348. In terms of the maximum productive use of electricity from the solar PV system, the first and second communities had the same value (0.39), while the third community's maximum had a value of 0.40. The developed model will be useful for evaluating the expected number of functional solar PV systems required, managing the quantity of electricity supply from the national grid and the generators, and planning electricity sharing for domestic and productive use within the selected communities.

Background: Landfill activities have environmental and economic values to a community. While the former deals with the control of landfill gas (LFG) emission into the atmosphere, the latter deals with the conversion of LFG to clean energy and subsequently, wealth creation. To fully harness these benefits, LFG plants must be well sited in a community, and this can be achieved when proper techno-economic analysis has been carried out. This will not only justify the LFG plants investment cost, but it will also guarantee their sustainability. Methods: This study presented a framework that ranks and selects LFG project for a community. First, numerical expressions were used to evaluate the techno-economic and environmental requirements of the project sites, then, the importance of these requirements was determined using Criteria Importance Through Inter-criteria Correlation (CRITIC) method. The sites were ranked using grey relational analysis (GRA) and Weighted Aggregated Sum-Product Assessment (WASPAS) method. An illustrative example of the proposed framework was presented using real-world dataset from different communities in Nigeria. Finally, four LFG project sites were ranked using four environmental criteria, five technical criteria, and eight economic criteria. Results: The results of WASPAS were verified by comparing them with the results of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and axiomatic design (AD) methods. It was observed that the results of both methods were the same for the different LFG sites. Conclusion: According to the results, it is obvious that this study will be useful to policy-makers and investors in LFG business, while the former could seek for plants’ sustainability, the latter interest will be on the payback period of their investment.

Business process evaluation is a common norm in small-medium-large industries globally and information obtained during such evaluation have been used in simulating future performance of most industries using mathematical models such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN). This study explored the possibility of predicting port productivity and berth effectiveness of seaport using ANN and ARIMA. A comparative analysis of multi-layer perceptron (MLP) back propagation algorithm and ARIMA performance was carried out based on ships days at port, days at berth and tonnage which serves as model inputs, while port productivity and berth effectiveness were the model outputs. The MLP-ANN and ARIMA (1, 0, 4- port productivity) and (1, 0, 4-berth effectiveness) results were compared based on their coefficient of correlation and mean square error. The coefficient of correlation for port productivity prediction using MLP-ANN was 0.998. This value outperformed that of ARIMA (0.9862) for port productivity. The coefficient of correlation of 0.9956 and 0.9928 were obtained for berth effectiveness using MLR and ARIMA, respectively.

Decision-making process is an integral part of every sporting event across the globe. This process uses inputs from single to multi-decision makers. To make a sporting event a success, appropriate decision making needs to be made starting from hosting-right to the end of the event. Thus, this study presents a framework that scientifically determines the hosting-right for a sporting event. The framework is based on techno-economic analysis of potential hosting locations for a sporting event. Techno-economic criteria are analysed using fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method. The analysis of the hosting-right is also performed through technical, economic, techno-economic and cost-benefit ratio perspectives. A case study of national sports festival in Nigeria is used to demonstrate the applicability of the proposed framework. Twelve technical and eight economic criteria are considered in the proposed framework. Six loca-tions are considered during the implementation of the proposed framework. The results obtained show that the issue of hosting-right award depends on the evaluation criteria that are considered by decision-makers.

Retrofitting technologies have helped to manage energy consumptions in residential, public and industrial buildings. However, understanding of the technical and economic considerations for selection of appropriate retrofitting technology is still evolving and divergent. Thus, this study presents a framework that combines techno-economic requirements as a means for evaluating the important retrofitting criteria and suitable lighting retrofit technologies for building projects. The framework is hinged on the unique features of entropy fuzzy and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. The analysis of the lighting technology selection was performed from technical, economic and techno-economic perspectives. During the application of the proposed framework, four lighting technologies (CFL, T5, E-ballast and T8-electronic) and nine techno-economic criteria were considered. The most and least important techno-economic criteria for the case study were net present value and electricity saved, respectively. The least and most suitable retrofitting technologies were T8-electronic and CFL, respectively, from techno-economic perspective. T5 and T8-electronic were identified as the most suitable lighting technologies from an economic and technical perspectives, respectively. This discrepancy in the results justified the need for the techno-economic approach for the retrofitting technologies evaluation.

The growth in renewable energy study has resulted in the expansion of mini-grids projects across different communities in developing countries. However, there is a need to ensure that a suitable business model is selected during the provision of mini-grid services in developing countries. This study presents a framework for ranking mini-grid business models. The proposed framework is a multi-criteria multi-decision (MCMD) making tool. The framework combines Criteria Importance Through Inter-criteria Correlation (CRITIC), TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) and WASPAS (weighted aggregated sum product assessment) methods. An interval type-2 fuzzy sets (IT2FSs) is used to convert the responses from decision-makers into crisp values. The applicability of the proposed framework was tested using Nigeria as a case study. Four potential mini-grid (private, government, community-based and public-private) business models for the case study were ranked using twelve criteria. The average values for the subjective and objective weight methods showed that electricity traffic and energy demand were the least and the most important criteria, respectively, for selecting a mini-grid business model. The results obtained showed that the best mini-grid model was a private model using a TOPSIS framework. A WASPAS framework ranked the community-based business model as the best model. This study results showed mini-grid business models in developing countries should be either private or community-based business models.

Despite experiencing industrialisation, Nigerian business organisations are largely in turbulent environments and have a significant need to manage their inventories effectively in collaboration with their production and maintenance departments. Research on this issue is still incomplete. The objective of this communication is to demonstrate the use of a system dynamics (SD) method in the enhancement of inventory practices in relation to maintenance engineering in a factory. As a method, SD showcases an enhanced insight into the complexity of dynamics associated with inventory cum maintenance practices. The SD technique was successfully employed to model and analyse variables to gain insight and pin-point the principal compelling factors in inventory management system of manufacturing organisations. A fuzzy-SD model was used to evaluate the stock levels of finished goods, the number of experienced technicians and in the number of unavailable machines in manufacturing systems. The proposed model is a combination of a fuzzy inference system, SD, TOPSIS, the WASPAS method and aggregated rank sum. Enhancement of inventory performance is displayed as a workable model that can be implemented to handle complicated inventories in factories to achieve improved performance in manufacturing.

The improvement in the provision of banking services to customers enhances bank’s performance (profitability and productivity) and the amounts of dividend declared to shareholders as well as bank’s competitiveness. One means of fast tracking the service time for bank customers is through the use of self-servicing machines, such as automated teller machine (ATM). Total service cost, expected waiting time in queue, ATM utilization and percentage of customer loss are some of the performance indices that are used to evaluate the service rendered by a bank’s ATM. This study proposes a framework for evaluating the performance of ATM by integrating queuing model and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology. Applicability of the framework was tested using practical data obtained from four banks in Nigeria. It was observed that the average ATM usage in the study area was less than 50%. The TOPSIS results identified Bank A as the best ranked bank. In addition, the results obtained revealed that banks with two ATM were ranked higher than banks with more than two ATM

Prediction is necessary in industrial practices according to the norms of modern scientific development. Recognising maintenance problems before they occur potentially helps to improve workforce performance as effective preventive maintenance actions are developed. A large number of industrial systems worldwide owe their gains to predictive analysis. Furthermore, precise predictions can aid maintenance policy makers in making decisions as well as promoting the quality of decision making in maintenance, particularly on maintenance workforce issues. This study reports the use of grey relational analysis cum artificial neural networks (ANN) for maintenance workforce size prediction through the selection of an optimal neural network architecture. Workforce cost, workload, productivity and effectiveness represent the input parameters utilised in the ANN framework. The method competitively determines the most suitable ANN architecture for maintenance workforce size. Comparison of the ANN model results reveal that its performance is better than a fuzzy inference system. Conclusively, the application of the framework advanced in this work was found useful with practical data obtained from a process industry that operates as a brewery. The efficiency of the proposed approach was documented.

In manufacturing systems, stiff competition in the international markets coupled with the global economic crisis has exerted immense pressure to search for new maintenance approaches. This study focuses on determining the optimal maintenance time and workforce size, as well as a low overall life-cycle cost. Current literature has completely ignored the influence of these measures and the significance of prediction has been downplayed. Consequently, the necessity to develop an approach based on scientific foundations is affirmed. Keeping in view the status of the literature concerning this issue, a surrogate model (an evolutionary algorithm technique and fuzzy inference system (FIS)) for maintenance workforce cost prediction is presented. The key influencing parameters include production volume, routine maintenance time, workforce size and spare parts costs. The surrogate model was tested using process industry data. The result (root mean square error) obtained from the surrogate model was better than those of an artificial neural network (ANN) and FIS. The implication of these results is that the surrogate model could serve as a basis for determining maintenance workforce costs during decision-making processes. The novelty of this study is that it uses a differential evolution algorithm to improve the performance of FIS when dealing with maintenance workforce cost prediction.

Maintenance workforce evaluation has recently received an increased attention in industries due to the significant influence it has on successful manufacturing. Great efforts, from the viewpoints of theory and practice have been taken in the development of the subject. However, its optimisation when considering uncertainties is not addressed adequately in literature. According to this requirement, a novel approach is contributed, providing an understanding for the uncertainties while combining factors for workforce size determination using an integrated Taguchi-fuzzy technique. The feasibility of a fuzzy maintenance workforce model as an expert tool was investigated and the results were validated with a literature model. The study observed that the fuzzy workforce prediction and optimisation tool could be used to avoid complex mathematical expressions for workforce size prediction by the maintenance manager. Compared to ARIMA, the model competes with ARIMA results and can be developed easily by maintenance mangers.

Previous studies have shown that soft computing models are excellent predictive models for demand management problems. However, their applications in solving water demand forecasting problems have been scantily reported. In this study, feedforward artificial neural networks (ANNs) and a support vector machine (SVM) were used to forecast water consumption. Two ANN models were trained using different algorithms: differential evolution (DE) and conjugate gradient (CG). The performance of these soft computing models was investigated with real-world data sets from the City of Ekurhuleni, South Africa, and compared with conventionally used exponential smoothing (ES) and multiple linear regression (MLR). The results obtained showed that the ANN model that was trained with DE performed better than the CG-trained ANN and other predictive models (SVM, ES and MLR). This observation further demonstrates the robustness of evolutionary computation techniques amongst soft computing techniques.