Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation, load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems, thereby enhancing overall grid performance and the efficacy of renewable energy integration.
{"title":"Enhancing Power Quality with PDO-FOPID Controller in Unified Power Quality Conditioner for Grid-Connected Hybrid Renewables","authors":"S. Nagaraju, B. Chandramouli","doi":"10.4314/njtd.v20i4.1805","DOIUrl":"https://doi.org/10.4314/njtd.v20i4.1805","url":null,"abstract":"Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation, load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems, thereby enhancing overall grid performance and the efficacy of renewable energy integration. ","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"151 S302","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Gbenebor, C. Odili, V.D. Obasa, E. F. Ochulor, S.O. Kusoro, O.C. Udogu-Obia, S.O. Adeosun
Polylactide (PLA) is a biodegradable polymer with low elongation which limits its use in some applications. The incorporation of biowaste particles has been employed to improve its properties. This work thus examines the impact of lignin particles reinforced on electrospun PLA fibre mats. Acid hydrolysis (1M of HCl at 60 and 100 oC for 2 and 4 h was used to extract lignin from Cocos nucifera L (CNHL) and Zea Mays Chaff (CCL). Lignin particles were added to molten PLA, stirred, and electrospun at 26 kV, using a static aluminum collector plate placed at 121mm from the spinneret tip. Morphological examination reveals that fibre diameter of neat PLA (9.7 µm) increased from 107 – 285 % with the additions of reinforcements. Maximum tensile strength of 1.03 MPa is recorded for PLA/CNHL 60oC /2 h. This composite maintains the highest elongation of 0.069 % compared to neat PLA (0.046 %). X-Ray diffractometer (XRD) result informs that the crystallinity of neat PLA (67.6 %) improves by 3%, with the use of CNHL 60 oC/ 2 h. Thermo gravimetric analysis (TGA) result shows that both fibre composites possess better thermal stability (380 oC) compared to reinforcing PLA fibre (319 oC).
{"title":"Morphological, Mechanical and Thermal Characteristics of PLA /Cocos nucifera L Husk and PLA/Zea mays Chaff Lignin Fibre Mats Composites","authors":"O. Gbenebor, C. Odili, V.D. Obasa, E. F. Ochulor, S.O. Kusoro, O.C. Udogu-Obia, S.O. Adeosun","doi":"10.4314/njtd.v20i4.1561","DOIUrl":"https://doi.org/10.4314/njtd.v20i4.1561","url":null,"abstract":"Polylactide (PLA) is a biodegradable polymer with low elongation which limits its use in some applications. The incorporation of biowaste particles has been employed to improve its properties. This work thus examines the impact of lignin particles reinforced on electrospun PLA fibre mats. Acid hydrolysis (1M of HCl at 60 and 100 oC for 2 and 4 h was used to extract lignin from Cocos nucifera L (CNHL) and Zea Mays Chaff (CCL). Lignin particles were added to molten PLA, stirred, and electrospun at 26 kV, using a static aluminum collector plate placed at 121mm from the spinneret tip. Morphological examination reveals that fibre diameter of neat PLA (9.7 µm) increased from 107 – 285 % with the additions of reinforcements. Maximum tensile strength of 1.03 MPa is recorded for PLA/CNHL 60oC /2 h. This composite maintains the highest elongation of 0.069 % compared to neat PLA (0.046 %). X-Ray diffractometer (XRD) result informs that the crystallinity of neat PLA (67.6 %) improves by 3%, with the use of CNHL 60 oC/ 2 h. Thermo gravimetric analysis (TGA) result shows that both fibre composites possess better thermal stability (380 oC) compared to reinforcing PLA fibre (319 oC). ","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis.
{"title":"Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction","authors":"U. A. Dodo, M. A. Dodo, A.F. Shehu, Y.A. Badamasi","doi":"10.4314/njtd.v20i4.1856","DOIUrl":"https://doi.org/10.4314/njtd.v20i4.1856","url":null,"abstract":"Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis. ","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"57 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140431024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. H. Ibrahim, E. C. Ashigwuike, W. Oluyombo, A. A. Sadiq
Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.
{"title":"Optimal capacitor planning for power factor improvement using hybrid particle swarm and harmony search optimization","authors":"A. H. Ibrahim, E. C. Ashigwuike, W. Oluyombo, A. A. Sadiq","doi":"10.4314/njtd.v20i3.1825","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1825","url":null,"abstract":"Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Yahaya, H. A. Ajimotokan, J. A. Adebisi, I. I. Ahmed, T. K. Ajiboye, S. Abdulkareem, K. R. Ajao
This study examined the appraisal of wind resources of Ilorin, Nigeria for vortex-induced wind turbine power generation and off-grid electrification. The technical potential of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) was employed as a tool to generate an estimated wind resource of Ilorin city, using five different hub-heights (10, 30, 50, 70, and 90 m). A statistical analysis of wind characteristics for 21 years from 2001 to 2021 was carried out using Weibull distribution function. The daytime and night-time wind characteristics were studied to determine prospective and investment hub-height(s). It was observed that the study area is a low wind region with a minimum and maximum mean wind speed of 2.89 m/s at 10 m and 7.68 m/s at 90 m, respectively. Wind turbines with cut-in wind speed of 2, 2.5, and 3 m have operational chances of 98%, 95% and 88%, respectively. Wind power density at 10, 30, and 50 m elevations was classified as poor while at 70 and 90 m elevations, was regarded as marginal and fair, respectively.
{"title":"Wind resource of Ilorin City for vortex-induced wind turbine power generation and off-grid electrification","authors":"T. Yahaya, H. A. Ajimotokan, J. A. Adebisi, I. I. Ahmed, T. K. Ajiboye, S. Abdulkareem, K. R. Ajao","doi":"10.4314/njtd.v20i3.1795","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1795","url":null,"abstract":"This study examined the appraisal of wind resources of Ilorin, Nigeria for vortex-induced wind turbine power generation and off-grid electrification. The technical potential of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) was employed as a tool to generate an estimated wind resource of Ilorin city, using five different hub-heights (10, 30, 50, 70, and 90 m). A statistical analysis of wind characteristics for 21 years from 2001 to 2021 was carried out using Weibull distribution function. The daytime and night-time wind characteristics were studied to determine prospective and investment hub-height(s). It was observed that the study area is a low wind region with a minimum and maximum mean wind speed of 2.89 m/s at 10 m and 7.68 m/s at 90 m, respectively. Wind turbines with cut-in wind speed of 2, 2.5, and 3 m have operational chances of 98%, 95% and 88%, respectively. Wind power density at 10, 30, and 50 m elevations was classified as poor while at 70 and 90 m elevations, was regarded as marginal and fair, respectively.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. A. Ubi, N. A. Ademoh, A. S. Abdulrahman, A. B. Hassan, J. D. Dashe, S. W. Oyeyemi, F. Ngolemasango
Carbon black and silica fillers have been widely used as reinforcing fillers in tyres and engine mounts. However, both fillers are non-renewable and with REACH legislation in Europe, the USA and elsewhere, where some of these fillers are termed hazardous due to the presence of polyaromatic hydrocarbons (PAHs), there is a need to search for sustainable alternative fillers to wholly or partially replace carbon black as a filler. This research studied rice husks-derived silica (RHS) as a filler in natural rubber (NR). The characteristics of RHS at 50 phr to 90 phr filler loading levels were examined to determine its suitability as a substitute for unsustainable carbon black (N772) fillers used in the rubber industry. Bound rubber content, crosslink density, tensile strength, young modulus, tear strength, shore A hardness, compressive set, and elongation at break were measured. Regression models were generated and the correlation of determination (R2) values was obtained. The RHS composites resulted in maximum tensile strength of 13.20 MPa at 90 phr, tear strength of 119 MPa at 90 phr, shore A hardness of 69 at 90 phr, compressive set of 6.72% at 80 and 90 phr, elongation at break of 453.60% at 80 phr, bound rubber content of 92.14% at 50 phr and crosslink density of 3.87×10-2 mol/cm3 at 70 phr. The results obtained were within the range of those obtained for the carbon black filled composites across various loading levels. The R2 value of mechanical characteristics for the RHS and N772 samples respectively were 50.06% and 62.18% (bound rubber content), 97.62% and 99.85% (tensile strength), 98.44% and 63.97% (tear strength), 89.16 and 97.40% (Shore A hardness), 32.90% and 91.80% (compressive set), a d 50.91% and 46.91% (elongation at break). Rice husk-derived Silica filled natural rubber composites showed favourable mechanical properties and can substitute traditional fillers in tyres, rubber engine mounts, bushings, seals and doormats.
{"title":"Mechanical characteristics and regression models of rice husk silica reinforced natural rubber composites","authors":"P. A. Ubi, N. A. Ademoh, A. S. Abdulrahman, A. B. Hassan, J. D. Dashe, S. W. Oyeyemi, F. Ngolemasango","doi":"10.4314/njtd.v20i3.1695","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1695","url":null,"abstract":"Carbon black and silica fillers have been widely used as reinforcing fillers in tyres and engine mounts. However, both fillers are non-renewable and with REACH legislation in Europe, the USA and elsewhere, where some of these fillers are termed hazardous due to the presence of polyaromatic hydrocarbons (PAHs), there is a need to search for sustainable alternative fillers to wholly or partially replace carbon black as a filler. This research studied rice husks-derived silica (RHS) as a filler in natural rubber (NR). The characteristics of RHS at 50 phr to 90 phr filler loading levels were examined to determine its suitability as a substitute for unsustainable carbon black (N772) fillers used in the rubber industry. Bound rubber content, crosslink density, tensile strength, young modulus, tear strength, shore A hardness, compressive set, and elongation at break were measured. Regression models were generated and the correlation of determination (R2) values was obtained. The RHS composites resulted in maximum tensile strength of 13.20 MPa at 90 phr, tear strength of 119 MPa at 90 phr, shore A hardness of 69 at 90 phr, compressive set of 6.72% at 80 and 90 phr, elongation at break of 453.60% at 80 phr, bound rubber content of 92.14% at 50 phr and crosslink density of 3.87×10-2 mol/cm3 at 70 phr. The results obtained were within the range of those obtained for the carbon black filled composites across various loading levels. The R2 value of mechanical characteristics for the RHS and N772 samples respectively were 50.06% and 62.18% (bound rubber content), 97.62% and 99.85% (tensile strength), 98.44% and 63.97% (tear strength), 89.16 and 97.40% (Shore A hardness), 32.90% and 91.80% (compressive set), a d 50.91% and 46.91% (elongation at break). Rice husk-derived Silica filled natural rubber composites showed favourable mechanical properties and can substitute traditional fillers in tyres, rubber engine mounts, bushings, seals and doormats.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The declining global supply and sources of vegetable oil consumed across different parts of the world have become a source of growing concern. Finding alternative sources demands concerted efforts and studies on other agricultural products not adequately utilized. This study investigates the extraction of oil from Treculia africana seeds using n-hexane as a solvent. The effect of heat pre-treatment of the seed samples on the process was also investigated using oven-drying and sun-drying methods, respectively. The pre-treatment process had no effect on the physicochemical properties of the extract except the maximum yields at 60 min obtained as 42.5 and 40.31%, respectively. Characterization of the extract using the physicochemical properties of the oil showed specific gravity 0.931, saponification value 624.4 mgNaOHg-1oil, acid value 2.57 mgKOHg-1oil, and iodine value 14.13mg100g-1 which indicates its suitability for consumption, soap making, production of pharmaceuticals and as a lubricant. The Kinetics of the process which was studied under different temperatures and time intervals indicate a first-order reaction. Several thermodynamic parameters were determined such as activation energy, enthalpy, and entropy. These physicochemical properties indicate that the extract is comparable to the vegetable oil obtained from other sources. The kinetics and thermodynamics studies indicate the spontaneity of the process showing that the energy required to break the solute-solvent/solvent-solvent interaction was more significant than that required to maintain the bonds between them thereby favouring the forward reaction and product formation.
{"title":"Oil extraction from <i>Treculia africana</i> seeds: process conditions, kinetic and thermodynamic studies","authors":"O. O. Okwonna, A. A. J. Obuebite, I. J. Otaraku","doi":"10.4314/njtd.v20i3.1419","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1419","url":null,"abstract":"The declining global supply and sources of vegetable oil consumed across different parts of the world have become a source of growing concern. Finding alternative sources demands concerted efforts and studies on other agricultural products not adequately utilized. This study investigates the extraction of oil from Treculia africana seeds using n-hexane as a solvent. The effect of heat pre-treatment of the seed samples on the process was also investigated using oven-drying and sun-drying methods, respectively. The pre-treatment process had no effect on the physicochemical properties of the extract except the maximum yields at 60 min obtained as 42.5 and 40.31%, respectively. Characterization of the extract using the physicochemical properties of the oil showed specific gravity 0.931, saponification value 624.4 mgNaOHg-1oil, acid value 2.57 mgKOHg-1oil, and iodine value 14.13mg100g-1 which indicates its suitability for consumption, soap making, production of pharmaceuticals and as a lubricant. The Kinetics of the process which was studied under different temperatures and time intervals indicate a first-order reaction. Several thermodynamic parameters were determined such as activation energy, enthalpy, and entropy. These physicochemical properties indicate that the extract is comparable to the vegetable oil obtained from other sources. The kinetics and thermodynamics studies indicate the spontaneity of the process showing that the energy required to break the solute-solvent/solvent-solvent interaction was more significant than that required to maintain the bonds between them thereby favouring the forward reaction and product formation.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. O. Amole, S. Oladipo, D. Ighravwe, K. A. Makinde, J. Ajibola
Energy is a fundamental human need for several activities. Energy can be impacted by several factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and the electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, GRU model can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that it can help the government and other stakeholders in economic planning of electricity distribution networks.
{"title":"Comparative analysis of deep learning techniques based COVID-19 impact assessment on electricity consumption in distribution network","authors":"A. O. Amole, S. Oladipo, D. Ighravwe, K. A. Makinde, J. Ajibola","doi":"10.4314/njtd.v20i3.1375","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1375","url":null,"abstract":"Energy is a fundamental human need for several activities. Energy can be impacted by several factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and the electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, GRU model can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that it can help the government and other stakeholders in economic planning of electricity distribution networks.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medium chain fatty acids (MCFAs) are fatty acids containing 6 to 12 carbon atoms with a wide range of industrial application. They can be produced by the fermentation of waste biomass through a process called chain elongation (CE). During CE, the type of inoculum used plays a key role in determining the optimal yield of MCFAs. In this study, we showed, for the first time, the use of three different inocula including leachate, rumen fluid and digestate from a biogas reactor for the batch fermentation of ensiled potato peels for MCFAs production. Results showed that the highest chain elongation was obtained when leachate was used as inoculum with a maximum yield of 57, 4 and 26 g/kgVS for caproic acid, heptanoic acid and caprylic acid respectively. A kinetic study shows that the production of MCFAs from ensiled potato peels was better described by the first-order model than by the modified Gompertz model.
{"title":"Production of medium chain fatty acids from ensiled potato peels; effect of inoculum type and kinetic study","authors":"J. A. Undiandeye, S. Kiman, J. V. Anaele","doi":"10.4314/njtd.v20i3.1383","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1383","url":null,"abstract":"Medium chain fatty acids (MCFAs) are fatty acids containing 6 to 12 carbon atoms with a wide range of industrial application. They can be produced by the fermentation of waste biomass through a process called chain elongation (CE). During CE, the type of inoculum used plays a key role in determining the optimal yield of MCFAs. In this study, we showed, for the first time, the use of three different inocula including leachate, rumen fluid and digestate from a biogas reactor for the batch fermentation of ensiled potato peels for MCFAs production. Results showed that the highest chain elongation was obtained when leachate was used as inoculum with a maximum yield of 57, 4 and 26 g/kgVS for caproic acid, heptanoic acid and caprylic acid respectively. A kinetic study shows that the production of MCFAs from ensiled potato peels was better described by the first-order model than by the modified Gompertz model.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed similarities and intra breed differences thereby creating a difficulty in their classification. The American Kennel Club (AKC) classified breeds of dog into groups based on characteristic, purpose, behaviuor and uses in order to optimize the potentials in the breeds. However, most people find it difficult to identify and classify the dog breed groups. Existing works did not consider the automatic grouping of dog breeds. Hence, there is need for automatic techniques to classify dog breeds into groups with improved accuracy. This work used the concept of Convolutional Neural Network (CNN) to develop a model that will automatically classify dog breeds into group based on the American Kennel Club standard using the Stanford’s dog dataset. The developed model achieved 92.2% accuracy, 80.0% sensitivity, 95.3% specificity and 93.4% area under curve (AUC). The model’s performance is excellent compared to existing works that used the same dataset. The experimental result was validated with two classic CNN models (ResNet-50 and SqueezeNet) using the same parameters.
{"title":"Automatic classification of breeds of dog using convolutional neural network","authors":"P.O. Adejumobi, I.O. Adejumobi, O.A. Adebisi, S.O. Ayanlade, I.I. Adeaga","doi":"10.4314/njtd.v20i3.1485","DOIUrl":"https://doi.org/10.4314/njtd.v20i3.1485","url":null,"abstract":"Dog is a mammal that has been a friend of man for ages, it is naturally a domestic animal with a high level of phenotype differences in behaviour and morphology. Breeding and crossbreeding activities have increased the number of dog breeds globally, thereby resulting in dogs with inter breed similarities and intra breed differences thereby creating a difficulty in their classification. The American Kennel Club (AKC) classified breeds of dog into groups based on characteristic, purpose, behaviuor and uses in order to optimize the potentials in the breeds. However, most people find it difficult to identify and classify the dog breed groups. Existing works did not consider the automatic grouping of dog breeds. Hence, there is need for automatic techniques to classify dog breeds into groups with improved accuracy. This work used the concept of Convolutional Neural Network (CNN) to develop a model that will automatically classify dog breeds into group based on the American Kennel Club standard using the Stanford’s dog dataset. The developed model achieved 92.2% accuracy, 80.0% sensitivity, 95.3% specificity and 93.4% area under curve (AUC). The model’s performance is excellent compared to existing works that used the same dataset. The experimental result was validated with two classic CNN models (ResNet-50 and SqueezeNet) using the same parameters.","PeriodicalId":31273,"journal":{"name":"Nigerian Journal of Technological Development","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135760881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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