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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.
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Microstructural characterization and hardness properties of magnesium alloy processed by high pressure torsion

- Sanusi, Kazeem O., Madushele, Nkosinathi, Akinlabi, Esther Titilayo

Life cycle assessment of a biogas digester : case study of a South African system

- Madushele, Nkosinathi

  • Authors: Madushele, Nkosinathi
  • Date: 2018
  • Subjects: Biogas , Product life cycle - Environmental aspects , Greenhouse gases - Environmental aspects , Biomass gasification
  • Language: English
  • Type: Doctoral (Thesis)
  • Identifier: http://hdl.handle.net/10210/263076 , uj:27804
  • Description: D.Ing. (Mechanical Engineering Sciences) , Abstract: Sustainable energy is a huge point of discussion amongst policy makers and academics alike. This stems from an increase in the world’s population, with shrinking finite energy sources that are currently used. The University of Johannesburg seeks to address this challenge through the development of a biogas digester plant. The study analysed a gate to gate model of a domestic biogas digester, with the intention of both evaluating the environmental impact of the University’s biogas digester, while also making use of fundamental computations in performing a Life Cycle Assessment initiative, as opposed to using commercially available software. This was done in the hopes of gaining deeper understanding on the computational structure of Life Cycle Assessments, and this can then be translated to developing more region specific databases for future studies. It was found that the designed digester produces more greenhouse gases (GHGs) during operation, than when the digester is manufactured and commissioned. This enabled a design alteration that minimised the GHGs prior to the completion of the design. Amongst a number of environmental impacts investigated, it is interesting to note that during the operational stage of the digester, there are chemicals that contribute to photochemical ozone depletion, and that in turn resulted in the recommendation of revising mechanical equipment that was initially proposed by the designer.
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Effect of heat treatment on microstructure and mechanical properties of magnesium alloy

- Sanusi, Kazeem O., Madushele, Nkosinathi, Akinlabi, Esther Titilayo

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

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

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).
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Data showing the effects of disc milling time on the composition and morphological transformation of (aþb) titanium alloy (Tie6Ale2Sne2Moe2Cre2Zr-0.25Si) grade

- Ogbonna, Okwudili Simeon, Akinlabi, Stephen A., Madushele, Nkosinathi

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.
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A streamlined life cycle assessment of a coal-fired power plant- the South African case study

- Dunmade, Israel, Madushele, Nkosinathi, Adedeji, Paul A., Akinlabi, Esther Titilayo

  • Authors: Dunmade, Israel , Madushele, Nkosinathi , Adedeji, Paul A. , Akinlabi, Esther Titilayo
  • Date: 2019
  • Subjects: Coal cycle, Coal-fired power plant, Environmental sustainability
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/396624 , uj:32939 , Citation : Dunmade, I. et al. 2019. A streamlined life cycle assessment of a coal-fired power plant- the South African case study
  • Description: Abstract : Non-renewable energy sources have detrimental environmental effects, which directly and indirectly affect the biosphere as environmental deposits from their use for energy generation exceed a threshold. This study performs a streamlined lifecycle assessment (LCA) of a coal-fired plant in South Africa. The cradle-to-grave LCA focuses on the coal cycle to determine hotspots with high environmental impacts in the process. Four impact categories were considered in this study; global warming potential, photochemical ozone creation potential, eutrophication potential, and acidification potential. Coal transportation, coal pulverization, water use, and ash management were identified as hotspots in the coal cycle. The coal process has 95% potential for global warming, 4% potential for eutrophication, 1% potential for acidification and a negligible percentage for photochemical ozone creation. Susceptibility to climate change, eutrophication, acid rain, soil degradation and water contamination among others are major concerns of the coal cycle. Outsourcing coal from nearby mines with train as medium of transportation reduces environmental impact. Similarly, the use mitigation technologies like flue gas desulphurization, carbon capture storage or selective catalytic reduction will reduce concentration of flue gas emitted. Ultimately, substituting the coal process with renewable energy sources will ensure environmental sustainability in South Africa. This study will serve as a good resource for further studies on LCA of coal power plants not only in other African countries but in other developing countries with similar situation.
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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.
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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.
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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.
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Hybrid neurofuzzy wind power forecast and wind turbine location for embedded generation

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

  • Authors: Adedeji, Paul A. , Akinlabi, Stephen A. , Madushele, Nkosinathi , Olatunji, Obafemi O.
  • Date: 2020
  • Subjects: ANFIS , Embedded generation genetic algorithm , Genetic algorithm
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436546 , uj:37871
  • Description: Abstract: , Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers.
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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.
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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.
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Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

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

  • Authors: Adedeji, Paul A. , Akinlabi, Stephen A. , Madushele, Nkosinathi , Olatunji, Obafemi O.
  • Date: 2020
  • Subjects: ANFIS-based modeling , GIS , MCDM
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/436562 , uj:37873 , Adedeji, P.A. 2020:Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review. DOI: https://doi.org/10.1016/j.jclepro.2020.122104
  • Description: Abstract: , Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning.
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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.
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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.
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