Showing items 1 - 17 of 17

Your selections:

A survey of artificial neural network-based prediction models for thermal properties of biomass

- Olatunji, Obafemi, Akinlabi, Stephen, Oluseyi, Ajayi, Madushele, Nkosinathi

  • Authors: Olatunji, Obafemi , Akinlabi, Stephen , Oluseyi, Ajayi , Madushele, Nkosinathi
  • Date: 2019
  • Subjects: ANN , Biomass , Heating value
  • Language: English
  • Type: Conference proceedings
  • Identifier: http://hdl.handle.net/10210/398288 , uj:33150 , Citation: Olatunji, O. et al. 2019. A survey of artificial neural network-based prediction models for thermal properties of biomass.
  • Description: Abstract: The global community has supported the need for sustainable and renewable energy due to environmental concerns from the greenhouse gas emission. Biomass stands as one of the most abundant and predictable sources of renewable energy. Therefore, to explore the maximum potential of biomass, a detailed understanding of its embedded potential is needed. However, most experimental procedures require equipment that is highly sophisticated and expensive. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass thermal properties. Artificial Neural Network (ANN) is proving to be a vital tool that can enhance the research development in biomass energy prediction. This review highlights the stages in ANN modeling and the application of ANN in Biomass thermal value prediction. It identifies the research gaps in the current status of research on ANN as related to biomass and the direction for further study.
  • Full Text:

Blended tropical almond residue for fuel production: characteristics, energy benefits, and emission reduction potential

- Olatunji, Obafemi O., Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A., Ndolomingo, Matumuene J., Thivhani, Meshack

  • Authors: Olatunji, Obafemi O. , Akinlabi, Stephen , Madushele, Nkosinathi , Adedeji, Paul A. , Ndolomingo, Matumuene J. , Thivhani, Meshack
  • Date: 2020
  • Subjects: Blended tropical almond , Clean fuel production , CO2 emission
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436512 , uj:37867 , Olatuni, O.O. et al. 2020: Blended tropical almond residue for fuel production: characteristics, energy benefits, and emission reduction potential. DOI: https://doi.org/10.1016/j.jclepro.2020.122013
  • Description: Abstract: , Besides the nuts produced from almond cultivation, it also generates several million tonnes of residue that include hulls, shells, leaves, pruning, and inedible kernels which are valuable feedstocks in clean fuel production. In this article, blended tropical almond residue of two particle sizes (NT15 and NT25) were investigated. The heating, proximate and ultimate values were reported while the chemical composition of the ash was determined. Also, the pore structure and the inherent functional groups were determined for the particle sizes. The thermogravimetric analysis was also carried out to determine the thermal behaviour at different heating rate (10, 15, 30 oCmin-1) in inert environment while the kinetic parameters were evaluated based on three non-isothermal methods (Flynn– Wall–Ozawa, Kissinger–Akahira–Sunose and distributed activation energy model). Notably, the ash content was higher in the finer particle size NT15 (1.11 %) compared to NT25 (0.87 %). Low pore surface area (1.218-0.970 m²g-1) agrees with literature values while a slight difference in pore size distribution was observed during adsorption at higher relative pressure. A representation of mixed functional groups whose wavelength falls within 527 cm-1, 848 cm-1, 991 cm-1, 1035 cm-1, 1179 cm-1, 1597 cm-1, 1772 cm-1, 2849 cm-1 was observed with no significant difference between the two particle sizes. The average activation energy, Ea for NT15 and NT25 were in the range of 127.4-131 kJmol-1 and 129-133 kJmol-1 respectively for all the three methods, with the lowest Ea (127.4 kJmol-1) and compensation factor, K0 (1.29E+12 min-1) obtained for the smaller particle size (NT15) based on Kissinger–Akahira–Sunose method. Finally, the energy benefits and CO2 emission reduction potential were estimated. The highest energy potential is in USA (4.17 Mtoe) while Morocco has the highest emission reduction at 3.28 %. The information obtained from this study can be used in the scaling up of bioreactors which can further support the global clean energy drive and reduce environmental pollution.
  • Full Text:

Estimation of municipal solid waste (MSW) com-bustion enthalpy for energy recovery

- Olatunji, Obafemi, Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A.

  • Authors: Olatunji, Obafemi , Akinlabi, Stephen , Madushele, Nkosinathi , Adedeji, Paul A.
  • Date: 2019
  • Subjects: Renewable energy, Climate change, Energy recovery
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/396616 , uj:32938 , Citation : Olatunji, O. 2019. Estimation of municipal solid waste (MSW) com-bustion enthalpy for energy recovery , 10.4108/eai.11-6-2019.159119
  • Description: Abstract : The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).
  • Full Text:

Geospatial investigation of physico-chemical properties and thermodynamic parameters of biomass residue for energy generation

- Olatunji, Obafemi O., Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A., Ndolomingo, Matumuene J.

  • Authors: Olatunji, Obafemi O. , Akinlabi, Stephen , Madushele, Nkosinathi , Adedeji, Paul A. , Ndolomingo, Matumuene J.
  • Date: 2020
  • Subjects: Corn cob , Geospatial investigation , Physicochemical properties
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436538 , uj:37870
  • Description: Abstract: , Biomass represents vast under-explored feedstock for energy generation across the globe. Among other factors, the location from where the feedstock is harvested may affect the overall properties and the efficiency of bioreactors used in the conversion process. Herein is reported some physicochemical properties, the kinetic study and thermodynamic analysis of corn cob sourced from two major economies in sub-Sahara African region. Brunauer Emmett and Teller (BET) analysis was performed to investigate the surface characteristics of corn cob while Fourier Transform Infrared Spectroscopy (FTIR) revealed the corresponding functional group present in the selected biomass residue. The proximate and CHNSO analyses were performed using the standard equipment and following the standard procedures, then the result is reported and compared based on the geographical locations under consideration. Also, the thermal decomposition study was carried out at different heating rate (10, 15, 30 Cmin-1) in inert atmosphere while the kinetic parameters were evaluated based on Flynn–Wall–Ozawa (FWO), and Kissinger–Akahira–Sunose (KAS) methods The Analysis of variance (ANOVA) showed that there is a statistically significant difference between ultimate constituents, the fixed carbon, and volatile matter obtained from the two countries at 95% confidence level. FTIR showed different spectra peak in both samples which means there are varying quantity of structural elements in each feedstock. The pore surface area (1.375 m²/g ) obtained for corncob from South Africa (SC25) was greater than the value (1.074 m²/g ) obtained for Nigeria (NC25). From the result, the highest value of activation energy, (Ea =190.1 kJmol-1 and 189.9 kJmol-1) was estimated for SC25 based on KAS and FWO methods respectively. The result showed that geographical location may somewhat affect some energetic properties of biomass and further provides useful information about thermodynamic and kinetic parameters which could be deployed in the simulation, optimization and scale-up of the bioreactors for pyrolysis process.
  • Full Text:

Laser beam forming of 3 mm steel plate and the evolving properties

- Akinlabi, Stephen, Shukla, Mukul, Akinlabi, Esther Titilayo, Tshilidzi, Marwala

  • Authors: Akinlabi, Stephen , Shukla, Mukul , Akinlabi, Esther Titilayo , Tshilidzi, Marwala
  • Date: 2011
  • Subjects: Laser beam forming , Deformation , Elongated grains
  • Type: Article
  • Identifier: uj:5325 , ISSN 1307-6884 , http://hdl.handle.net/10210/8239
  • Description: This paper reports the evolving properties of a 3 mm low carbon steel plate after Laser Beam Forming process (LBF) To achieve this objective, the chemical analyse material and the formed components were carried out and compared; thereafter both were characterized through microhardness profiling, microstructural evaluation and tensile testing. The chemical analyses showed an increase in the elemental concentration of the formed component when compared to the as received material; this can be attributed to the enhancement property of the LBF process. The Ultimate Tensile Strength (UTS) and the Vickers microhardness of the formed component shows an increase when compared to the as received material, this was attributed to strain hardening and grain refinement brought about by the LBF process. The microstructure of the as received steel consists of equiaxed ferrit and pearlite while that of the formed component exhibits elongated grains.
  • Full Text:

Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste

- Olatunji, Obafemi O., Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A., Felix, Ishola

  • Authors: Olatunji, Obafemi O. , Akinlabi, Stephen , Madushele, Nkosinathi , Adedeji, Paul A. , Felix, Ishola
  • Date: 2019
  • Subjects: Municipal solid waste , High heating value , Multilayer perceptron
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/405733 , uj:34082 , Citation: Olatunji, O.O. et al. 2019. Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste.
  • Description: Abstract: Energy from municipal solid waste is steadily being integrated into the global energy feedstock, given the huge amount of waste being generated from various sources. This study develops a Multilayer Perceptron Artificial Neural Network for the prediction of High Heating Value of municipal solid waste as a function of moisture content, carbon, hydrogen, oxygen, nitrogen, sulphur, and ash. A total of 123 experimental data were extracted from reliable database for training, testing, and validation of the model. This model was trained, validated and tested with 70%, 20%, and 10% of the municipal solid waste biomass datasets respectively. The predicted High Heating Value was compared with the experimental data for two different training functions: Levenberg Marquardt backpropagation and Resilience backpropagation, and with some correlation from the literature. The accuracy of the model was reported based on some known performance criteria. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Coefficient of Correlation (CC) were 3.587, 2.409, 21.680, 0.970 respectively for RP and 3.095, 0.328, 22.483, 0.986 for LM respectively. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted High Heating Values (HHV). The authors concluded that these models can be a useful tool in the prediction of heating value of MSW in order to facilitate clean energy production from waste.
  • Full Text:

Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data

- Adedeji, Paul A, Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi

  • Authors: Adedeji, Paul A , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi
  • Date: 2019
  • Subjects: ANFIS , Electricity Consumption , FCM
  • Language: English
  • Type: Conference proceedings
  • Identifier: http://hdl.handle.net/10210/406893 , uj:34228 , Citation: Adedeji, P.A. et al. 2019 : Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data.
  • Description: Abstract : Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a mid-term forecast of electricity consumption. The model comprises of three meteorological variables as inputs and electricity consumption as output. Two ANFIS models with two clustering techniques (Fuzzy c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS- GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined using known statistical metrics. This compares the predicted electricity consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.
  • Full Text:

Non-linear autoregressive neural network (NARNET) with SSA filtering for a university energy consumption forecast

- Adedeji, Paul, Akinlabi, Stephen, Ajayi, Oluseyi, Madushele, Nkosinathi

Predicting the effect of seasonal variation on the physical composition of municipal solid waste : a case study of the City of Johannes-burg

- Adeleke, Oluwatobi, Akinlabi, Stephen, Hazzan, S., Jen, Tien-Chien

  • Authors: Adeleke, Oluwatobi , Akinlabi, Stephen , Hazzan, S. , Jen, Tien-Chien
  • Date: 2020
  • Subjects: Adaptive Neuro-Fuzzy Inference System , Season , Municipal Solid Waste
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/461222 , uj:41071 , Citation: Adeleke, O. et al. 2020. Predicting the effect of seasonal variation on the physical composition of municipal solid waste : a case study of the City of Johannes-burg.
  • Description: Abstract: Several factors influence the physical, chemical, and thermal properties of waste at different sources. One of the major indexes to variation in the morpho-logical composition of municipal solid waste is the season. A significant discrep-ancy in the composition of municipal solid waste at different seasons has been re-ported in the literature. However, this study explores the Adaptive Neuro-Fuzzy Inference System (ANFIS) with a fuzzy c-means (FCM) clustering technique to predict the physical content of waste in South Africa based on the varying weather parameters at different seasons. Four different models (I-IV) were developed to forecast the percentage fraction of Organics, Plastics, Paper, and Textile, respec-tively. The choice of these streams was because a closer look at the historical data reveals a significant variation in the percentage of these waste fractions at different seasons with little or no difference in other waste streams. The percentage compo-sition of samples of waste collected and characterized at Marie Louise Landfill, Jo-hannesburg in summer 2015 and winter 2016 was used as the output variable. Weather parameters for the same period were extracted from South Africa Weather Service data and used as the input variables. M-file script was written and computed on a workstation with configurations of 64 bits, 4GB ram Intel(R) core(TM) i3. The performance of the ANFIS models I-IV was evaluated using Mean Absolute Devi-ation (MAD), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).
  • Full Text:

Prediction of the heating value of municipal solid waste : a case study of the city of Johannesburg

- Adeleke, Oluwatobi, Akinlabi, Stephen, Jen, Tien-Chien, Dunmade, Israel

  • Authors: Adeleke, Oluwatobi , Akinlabi, Stephen , Jen, Tien-Chien , Dunmade, Israel
  • Date: 2020
  • Subjects: Physical Composition , Lower heating value , Municipal Solid Waste
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/461229 , uj:41073 , Citation: Adeleke, O. et al. 2020. Prediction of the heating value of municipal solid waste : a case study of the city of Johannesburg.
  • Description: Abstract: In this study, a municipality-based model was developed for predicting the Lower heating value (LHV) of waste which is capable of overcoming the demerit of generalized model in capturing the peculiarity and characteristics of waste generated locally. The city of Johannesburg was used as a case study. Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy-Inference System (ANFIS) models were developed using the percentage composition of waste streams such as paper, plastics, organic, textile and glass as input variables and LHV as the output variable. The ANFIS model used three clustering techniques, namely Grid Partitioning (ANFIS-GP), Fuzzy C-means (ANFIS-FCM) and Subtractive Clustering (ANFIS-SC). ANN architectures with a range of 1-30 neurons in a single hidden layer were tested with three training algorithms and activation functions. The GP-clustered ANFIS model (ANFIS-GP) outperformed all other models with root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) values of 0.1944, 0.1389 and 4.2982 respectively. Based on the result of this study, a GP-clustered ANFIS model is viable and recommended for predicting LHV of waste in a municipality.
  • Full Text:

Property-based biomass feedstock grading using k-Nearest Neighbour technique

- Olatunji, Obafemi O., Akinlabi, Stephen, Madushele, Nkosinathi, Adedeji, Paul A.

  • Authors: Olatunji, Obafemi O. , Akinlabi, Stephen , Madushele, Nkosinathi , Adedeji, Paul A.
  • Date: 2020
  • Subjects: Biomass classification , Energy , ��-NN classifier
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436402 , uj:37855 , Citation: Olatunji, O.O., Akinlabi, S. & Akinlabi, S. 2020. Property-based biomass feedstock grading using k-Nearest Neighbour technique.
  • Description: Abstract: Energy generation from biomass requires a nexus of different sources irrespective of origin. A detailed and scientific understanding of the class to which a biomass resource belongs is therefore highly essential for energy generation. An intelligent classification of biomass resources based on properties offers a high prospect in analytical, operational and strategic decision-making. This study proposes the 𝑘-Nearest Neighbour (𝑘-NN) classification model to classify biomass based on their properties. The study scientifically classified 214 biomass dataset obtained from several articles published in reputable journals. Four different values of 𝑘 (𝑘=1,2,3,4) were experimented for various self normalizing distance functions and their results compared for effectiveness and efficiency in order to determine the optimal model. The 𝑘-NN model based on Mahalanobis distance function revealed a great accuracy at 𝑘=3 with Root Mean Squared Error (RMSE), Accuracy, Error, Sensitivity, Specificity, False positive rate, Kappa statistics and Computation time (in seconds) of 1.42, 0.703, 0.297, 0.580, 0.953, 0.047, 0.622, and 4.7 respectively. The authors concluded that 𝑘-NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that 𝑘-NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins.
  • Full Text:

The future of renewable energy for electricity generation in sub-Saharan Africa

- Adedeji, Paul A, Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi

  • Authors: Adedeji, Paul A , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi
  • Date: 2019
  • Subjects: Non-linear autoregressive ANN , Renewable energy , sub-Saharan Africa
  • Language: English
  • Type: Conference proceedings
  • Identifier: http://hdl.handle.net/10210/406901 , uj:34229 , Citation: Adedeji, P.A. et al. 2019 : The future of renewable energy for electricity generation in sub-Saharan Africa.
  • Description: Abstract : Energy transition in the last decade has experienced increased quota of renewable energy in the global energy mix. In sub-Saharan Africa (SSA), the transition from the fossil fuel to the renewable energy source has been gradual. The state of renewable energy in the region in the next decade is the focus of this study. This study uses a single-layer perceptron artificial neural network (SLP-ANN) to backcast from 2015 to 2006 and forecast from 2016 to 2020 the percentage of renewable energy for electricity generation, exempting the hydropower in the energy mix of the SSA based on historical data. The backcast percentage renewable energy mix was evaluated using known statistical metrics for accuracy measures. The root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) obtained were 0.29, 0.18, and 14.69 respectively. The result shows possibility of an increase in the percentage of renewable energy in the electricity sector in the region. In 2020, the percentage of renewable energy in sub-Saharan region is expected to rise to 4.13% with exclusion of the hydropower. With government policies encouraging the growth of the renewable energy as a means of power generation in the region, the predicted percentage and even more can be realized.
  • Full Text:

The influence of scanning speed and number of scans on the properties of laser formed steel

- Sanusi, Kazeem Oladele, Akinlabi, Stephen, Akinlabi, Esther Titilayo

Towards low-carbon energy state in South Africa: a survey of energy availability and sustainability

- Adedeji, Paul A, Akinlabi, Stephen, Madushelel, Nkosinathi, Olatunji, Obafemi O

  • Authors: Adedeji, Paul A , Akinlabi, Stephen , Madushelel, Nkosinathi , Olatunji, Obafemi O
  • Date: 2019
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/405658 , uj:34073 , Citation: Adedeji, P.A., et al. 2019 : Towards low-carbon energy state in South Africa: a survey of energy availability and sustainability. DOI: 10.4108/e ai.13-7-2018.161751.
  • Description: Abstract : The drive towards low-carbon economy in South Africa has necessitated alternative energy sources for electricity generation. More alternative sources have evolved in recent times with a view to making energy available to all and sundry. However, asides proliferation of these sources and extensions in form of micro-grids, the questions of increased availability and sustainability has become a growing concern. This survey investigates the state of the renewable energy system in South Africa with focus on the elements, which enhance energy availability and sustainability in the emerging transition to a low- carbon economy. Case studies of other countries were reviewed and considered in the South African context. It was observed that energy availability on the journey to the low-carbon economy is influenced by physical, climatic, human, prosumer concept and political factors. In sustaining the transition and progressing to a green economy, intelligent use of data from power generation, transmission, and distribution sectors for intelligent data-driven decision-making processes was also found as essential. As part of the sustainability roadmap, efficiency at the end-user side of the value chain and a system thinking paradigm in the harvesting of renewable energy sources (RES) and formulation of supporting policies were also identified. In the overall, the study reveals that South Africa is replete with abundance of RES, however, their continuous availability and sustainability depends on joint interventions of both stakeholders and the government with viable environment for the growth of the sector.
  • Full Text:

Towards low-carbon energy state in South Africa: a survey of energy availability and sustainability

- Adedeji, Paul A., Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi O.

  • Authors: Adedeji, Paul A. , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi O.
  • Date: 2020
  • Subjects: Energy availability , Energy sustainability , Low-carbon state
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436570 , uj:37874 , Adedeji, P.A., et al. 2020: Towards low-carbon energy state in South Africa: a survey of energy availability and sustainability. DOI: http://dx.doi.org/10.4108/eai.13-7-2018.161751
  • Description: Abstract: , The drive towards low-carbon economy in South Africa has necessitated alternative energy sources for electricity generation. More alternative sources have evolved in recent times with a view to making energy available to all and sundry. However, asides proliferation of these sources and extensions in form of micro-grids, the questions of increased availability and sustainability has become a growing concern. This survey investigates the state of the renewable energy system in South Africa with focus on the elements, which enhance energy availability and sustainability in the emerging transition to a lowcarbon economy. Case studies of other countries were reviewed and considered in the South African context. It was observed that energy availability on the journey to the low-carbon economy is influenced by physical, climatic, human, prosumer concept and political factors. In sustaining the transition and progressing to a green economy, intelligent use of data from power generation, transmission, and distribution sectors for intelligent data-driven decision-making processes was also found as essential. As part of the sustainability roadmap, efficiency at the end-user side of the value chain and a system thinking paradigm in the harvesting of renewable energy sources (RES) and formulation of supporting policies were also identified. In the overall, the study reveals that South Africa is replete with abundance of RES, however, their continuous availability and sustainability depends on joint interventions of both stakeholders and the government with viable environment for the growth of the sector.
  • Full Text:

Towards sustainability in municipal solid waste management in South Africa : a survey of challenges and prospects

- Adeleke, Oluwatobi, Akinlabi, Stephen, Jen, Tien-Chien, Dunmade, Israel

  • Authors: Adeleke, Oluwatobi , Akinlabi, Stephen , Jen, Tien-Chien , Dunmade, Israel
  • Date: 2020
  • Subjects: Intelligent modeling , Circular economy , Sustainability
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/461231 , uj:41072 , Citation: Adeleke, O. et al. 2020. Towards sustainability in municipal solid waste management in South Africa : a survey of challenges and prospects.
  • Description: Abstract: In most developing countries, the huge amount of unmanaged municipal solid wastes and the inefficiency of the current waste management system has resulted in an unprecedented effect on human health and the quality of the environment. The drive towards sustainability in solid waste management in South Africa has led to the promulgation of several legislations and policies directed towards increased efficiency of solid waste management strategies. However, despite the progress in South Africa’s waste management systems over the years, it is still being constantly faced with some challenges and shortcomings. To achieve sustainable development through the transition from a linear economic model to a circular economy, there is a need to revamp the waste management sector. This study presents a survey of the key physical elements of integrated waste management in South Africa. The study further discusses the challenges with major emphasis on the future directions of integrated waste management. Waste management decisions are data-driven decisions. This study identifies the lack of accurate and reliable waste-related data as one of the major factors that impede the fast-track growth towards sustainable waste management in South Africa. A data-mining approach that emphasizes intelligent modeling of waste management systems is recommended to support the national waste database which will aid waste management decisions and optimizes waste management facilities and investments. Sustainability in waste management in South Africa requires a multi-sector intervention and involvement to stimulate sustainable development in waste management.
  • Full Text:

Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

- Adedeji, Paul A., Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi O.

  • Authors: Adedeji, Paul A. , Akinlabi, Stephen , Madushele, Nkosinathi , Olatunji, Obafemi O.
  • Date: 2020
  • Subjects: ANFIS , Autoregressive model , Data clustering
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436554 , uj:37872
  • Description: Abstract: , The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models.
  • Full Text:
  • 1
  • Disclaimer
  • Privacy
  • Copyright
  • Contact