Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

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

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 -Nearest Neighbour (-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 (=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 -NN model based on Mahalanobis distance function revealed a great accuracy at =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 -NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that -NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance

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

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

Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications

Higher heating value (HHV) is a key characteristic for the assessment and selection of biomass feedstocks as a fuel source. The HHV is usually measured using an adiabatic oxygen bomb calorimeter; however, this method can be time consuming and expensive. In response, researchers have attempted to use artificial neural network (ANN) systems to predict HHV using proximate and ultimate analysis data, but these efforts were hampered by varying case specific approaches and methodologies. Based on the complex ANN structures, a clear state of the art ANN understanding must be required for the prediction of biomass HHV. This study provides a comprehensive ANN application for HHV prediction in terms of how the activation functions, algorithms, hidden layers, dataset, and randomisation of the dataset affects the prediction of HHV of biomass feedstocks. In this paper we present a comparative qualitative and quantitative analysis of thirteen different algorithms, four different activation functions (logsig, tansig, poslin, purelin) with a wide range of hidden layer (3–15) for ANN models, used to predict the HHV of the biomass feedstocks. ANN models trained by the combination of ultimate-proximate analyses (UAPA) datasets provided more accurate predictions than the models trained by ultimate analysis or proximate analysis datasets. Regardless of the used datasets, sigmoidal activation functions (tansig and logsig) provide better prediction results than linear activation function (poslin and purelin). Furthermore, as training activation functions, “Levenberg-Marquardt (lm)” and “Bayesian Regularization (br)” algorithms provide the best HHV prediction. The best average correlation coefficients of 30 randomised run were observed with tansig as 0.962 and 0.876 for the ANN model developed by the UAPA dataset with a relatively high confidence levels of ∼96% for training and ∼92% for testing

Hydrogen production from plastic waste: A comprehensive simulation and machine learning study

Gasification, a highly efficient method, is under extensive investigation due to its potential to convert biomass and plastic waste into eco-friendly energy sources and valuable fuels. Nevertheless, there exists a gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) and polypropylene (PP) and its capability to produce hydrogen (H2) fuel. In this study a comprehensive process simulation using a quasi-equilibrium approach based on minimizing Gibbs free energy has been introduced. To enhance H2 content, a water-gas shift (WGS) reactor and a pressure swing adsorption (PSA) unit were integrated for effective H2 separation, increasing H2 production to 27.81 kg/h. To investigate the operating conditions on the process the effects of three key variables in a gasification reactor namely gasification temperature, feedstock flow rate and gasification pressure have been explored using sensitivity analysis. Furthermore, several machine learning models have been utilized to discover and optimize maximum capacity of the process for H2 production. The sensitivity analysis reveals that elevating the gasification temperature from 500 °C to 1200 °C results in higher production of H2 up to 23 % and carbon monoxide (CO). However, generating H2 above 900 °C does not lead to a significant upturn in process capacity. Conversely, an increase in pressure within the gasification reactor is shown to decrease the system capacity for generating both H2 and CO. Moreover, increasing the mass flow rate of the gasifying agent to 250 kg/h in the gasification reactor has shown to be merely productive in process capacity for H2 generation, almost a 5 % increase. Regarding pressure, the hydrogen yield decreases from 22.64 % to 17.4 % with an increase in pressure from 1 to 10 bar. It has been also revealed that gasification temperature has more predominant effect on Cold gas efficiency (CGE) compared to gasification pressure and Highest CGE Has been shown by PP at 1200 °C. Among the various machine learning models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99

Artificial Neural Network predictive model for Hydrogen production using Biomass gasification in a pilot plant

The growth in population nowadays has led to an increase in the consumption of the fossil fuels like oil and gas, which leads to depletion and shortage in the supply of the oil and gas. Also it will lead to an increase in the pollution and greenhouse effects in the environment. The need for a reliable, affordable and clean energy supply rises as it is very important for society, economy and the environment. Hydrogen production from biomass gasification is considered a very promising clean energy option for reduction of greenhouse gas emissions and energy dependency. The complexity of the biomass gasification process has led the researchers to develop models to simplify the process and save time and energy. A lot of models have been developed like the equilibrium model, kinetic model and the Artificial Neural Network (ANN) model. ANN models are simple to use, easy to generate and require a short period of time to get acceptable results depending on the pool of previous experimental data comparing to the other models that need power, time, a lot of assumptions and calculations to obtain good results. The main objectives of this study are: 1- to design and develop an Artificial Neural Network (ANN) model for the hydrogen production from biomass gasification process. 2- To evaluate the results of the model and validate them with the previous experimental data. 3- To compare the results of the simulation with different ANN models with the SIMCA-P software model. To achieve the goal of this study, four (4) ANNs have been developed after performing a preliminary analysis which was done by SIMCA-P11 and SIMCA-P13 software to determine the factors that affect the hydrogen production and also as it has a linear modelling for the process which is compared to the results of the ANNs. ANNs performed better in the prediction process with a mean squared error (MSE) of 5.4%. This validate that the ANN modelling is better for the purposes of prediction comparing to the other models available

Examining the Relationship Between Lignocellulosic Biomass Structural Constituents and Its Flow Behavior

Lignocellulosic biomass material sourced from plants and herbaceous sources is a promising substrate of inexpensive, abundant, and potentially carbon-neutral energy. One of the leading limitations of using lignocellulosic biomass as a feedstock for bioenergy products is the flow issues encountered during biomass conveyance in biorefineries. In the biorefining process, the biomass feedstock undergoes flow through a variety of conveyance systems. The inherent variability of the feedstock materials, as evidenced by their complex microstructural composition and non-uniform morphology, coupled with the varying flow conditions in the conveyance systems, gives rise to flow issues such as bridging, ratholing, and clogging. These issues slow down the conveyance process, affect machine life, and potentially lead to partial or even complete shutdown of the biorefinery. Hence, we need to improve our fundamental understanding of biomass feedstock flow physics and mechanics to address the flow issues and improve biorefinery economics. This dissertation research examines the fundamental relationship between structural constituents of diverse lignocellulosic biomass materials, i.e., cellulose, hemicellulose, and lignin, their morphology, and the impact of the structural composition and morphology on their flow behavior. First, we prepared and characterized biomass feedstocks of different chemical compositions and morphologies. Then, we conducted our fundamental investigation experimentally, through physical flow characterization tests, and computationally through high-fidelity discrete element modeling. Finally, we statistically analyzed the relative influence of the properties of lignocellulosic biomass assemblies on flow behavior to determine the most critical properties and the optimum values of flow parameters. Our research provides an experimental and computational framework to generalize findings to a wider portfolio of biomass materials. It will help the bioenergy community to design more efficient biorefining machinery and equipment, reduce the risk of failure, and improve the overall commercial viability of the bioenergy industry

Correlating LIBS Coal Data for Coal Property Prediction

This report presents results for correlations between coal data derived from laboratory analysis and Laser Induced Breakdown Spectroscopy analysis. LIBS data were used to predict higher order properties of coal using artificial neural network models. Higher order coal properties such as heating value and ash fusion temperature are predicted using LIBS analysis and compared against standard laboratory measurements. Selected formulas for the prediction of coal properties are also presented and compared against the neural network and laboratory results

Advanced Technologies for Biomass

The use of biomass and organic waste material as a primary resource for the production of fuels, chemicals, and electric power is of growing significance in light of the environmental issues associated with the use of fossil fuels. For this reason, it is vital that new and more efficient technologies for the conversion of biomass are investigated and developed. Today, various advanced methods can be used for the conversion of biomass. These methods are broadly classified into thermochemical conversion, biochemical conversion, and electrochemical conversion. This book collects papers that consider various aspects of sustainability in the conversion of biomass into valuable products, covering all the technical stages from biomass production to residue management. In particular, it focuses on experimental and simulation studies aiming to investigate new processes and technologies on the industrial, pilot, and bench scales