Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction

 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

In-depth physico-chemical characterisation and estimation of the grid power potential of municipal solid wastes in Abuja city

Ineffective municipal solid waste (MSW) management is one of the major impediments to the realisation of sustainable development goals by developing countries. Prudent waste quantification and characterisation are essential for the planning and execution of sound waste management, including energy recovery. This study, therefore, analysed the power generation potentials of MSW in Abuja, Nigeria for possible evacuation to the national grid. The city's MSW generation rate was determined followed by in-depth physico-chemical and thermal analyses of the waste samples taken from the Gosa dumpsite. The results revealed that plastic bottles (14.74%) and food residues (14.64%) dominated the waste streams. Furthermore, of the 257,628 tons/year of MSW produced at the rate of 0.53 kg/person/day, 69.17% can guarantee continuous and sustainable energy production via incineration. The energy, power, and grid power potentials of these waste components were found as 2,274.42 MWh, 28.43 MW, and 19.19 MW respectively. Compared to the on-grid thermal power stations in the country, this waste-to-energy pathway can save 67.5 million metric tons of carbon dioxide emissions per year. It is anticipated that this study will serve as a model for achieving the SDG agenda, particularly in Nigeria

Comparative study of different training algorithms in backpropagation neural networks for generalized biomass higher heating value prediction

When selecting biomass feedstock for sustainable heat and electricity generation, higher heating value (HHV) is an important consideration. Meanwhile, the laboratory procedures of using an adiabatic oxygen bomb calorimeter to determine the HHV are strenuous, costly, and time-consuming. As a result, researchers have turned to artificial intelligence techniques such as artificial neural networks (ANN) to predict HHV using data from proximate analysis. Notwithstanding, this approach has been hampered by different case-specific techniques and methodologies given the heterogeneous nature of biomass materials and intricate ANN structures. This study, therefore, examined and compared the efficacy of six training algorithms comprising thirteen distinct training functions of feedforward backpropagation neural networks to predict the HHV of a variety of biomass materials as a function of the proximate analysis. In creating the networks, the neurons of the hidden layer were iterated from 1 to 20 leading to 260 investigated scenarios. Compared to other training algorithms, the Bayesian Regularization and Levenberg-Marquardt with 15 and 12 hidden neurons respectively, demonstrated superior prediction performances based on the Nash-Sutcliff's efficiencies of 0.9044 and 0.8877, and mean squared errors of 0.002271 and 0.00267. It is envisaged that this study will create an insightful paradigm for a rapid selection of best-performing ANN algorithms for biomass HHV prediction

Modified Fractional Order PID Controller for Load Frequency Control of Four Area Thermal Power System

This paper presents the development of a modified Fractional Order Proportional Integral Derivative (FOPID) controller to mitigate frequency deviation in a four-area thermal power system. Change in load demand and noisy power system environment can cause frequency deviation. Reducing high-frequency deviation is very paramount in load frequency control. This is because large frequency deviation can cause the transmission line to be overloaded, which may damage transformers at the transmission level, damage mechanical devices at the generating stations and also damage consumer devices at the distribution level. The conventional PID has been widely used for this problem. However, the parameter values of the various generating units of the power system like generators, turbines and governors keep changing due to numerous on/off witching in the load side. As such, it is essential that the control strategy applied should have a good capability of handling uncertainties in the system parameters and good disturbance rejection. Fractional order PID controller is known to give a higher phase margin resulting in very good disturbance rejection, robustness to high-frequency noise and elimination of steady-state error. A four-area power system was designed, and FOPID was used as the supplementary controller to mitigate frequency deviation. Ant Lion Optimizer (ALO) algorithm was used to optimize the gains of the FOPID controller by minimizing Integral Square Error (ISE) as the objective function. Results obtained outperformed other designed methods available in the literature in terms of reducing frequency deviation, tie-line power deviation and area control error

Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model

The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features—building compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution—we elucidate the intricate relationships between building characteristics and their corresponding heating load (HL) and cooling load (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of unique architectural designs and building materials. Using a dataset encompassing 768 buildings, we demonstrate the prowess of our proposed models. Among the algorithms we employed, the extreme gradient boosting algorithm stands out, registering impressive accuracy metrics (HL: RSQ = 0.9986, RMSE = 0.3797, MAE = 0.2467 and MAPE = 1.1812; CL: RSQ = 0.9938, RMSE = 0.7578, MAE = 0.4546 and MAPE = 1.6365). We further integrate SHAP analysis, revealing that relative compactness positively influences both HL and CL the most, closely followed by surface area and glazing area. By merging an explainable extreme gradient boosting algorithm with a Bayesian-based metaheuristic optimization technique, we ensure both high predictive accuracy and interpretability. This study holds profound implications for enhancing building energy efficiency, curbing waste, and championing the shift to sustainable energy sources, aligning seamlessly with SDG 7