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Journal of Biochemical Technology, 2017
The main objective of this study is to determine the growth kinetics of mesophilic and thermophilic bacteria in biogas anaerobic digester using first order kinetic model, Monod kinetic model, diffusion model, Chen-Hashimoto model, Sing model and Cantois model. Nonlinear, stochastic models like artificial neural networks coupled with Monod kinetics was also applied for modeling the rate constants in anaerobic biogas digester. Thermophilic bacterial anaerobic digester is a found to be suitable for very hot weathers when compared with mesophilic bacterial anaerobic digester. Artificial neural network is proved to be an effective tool in predicting the rate equation when compared with other linear models.
2017
The modelling of anaerobic co-digestion of household food solid wastes and wastewater are complex and this is due to the rigorous processes that take place during the digestion process. The development of a predictive model that is capable of the simulation of anaerobic digester (AD) performances can go a long way in helping the operation of the AD processes and the optimization for biogas yield. The artificial neural networks (ANNs) approach is considered to be suitable and straightforward modelling method for AD process. In this research work, a multi-layer ANNs model with six input layer, ten hidden layers was trained using Lavenberg-Marquardt back propagation algorithm to simulate the digester operation and to predict the outcome of biogas yield. The performance of the developed ANNs models was validated and the results obtained from the research work reveal the effectiveness of the model to predict biogas yield with a mean squared error (MSE) of best validation performance of 5...
Artificial Neural Network Modelling for Biogas Production in Biodigesters
Chemical Engineering Transactions, 2019
The use of the biodigestion is considered promising for the energetic valorization of agriculture biomass such as swine farm sewage and lignocelulosic residues. The understanding of biodigesters operation and the control of their main operational variables are of great importance to improve the performance of anaerobic digestion process in order to increase biogas production. In this context, mathematical modelling can be used as a tool to increase process efficiency. This work presents the development of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) to predict volume of biogas. The variables from process were temperature (ºC), pH, FOS/TAC ratio and type of biodigesters. A database was constructed with the information of the experiments, dividing them into groups of training (67 %) and test (33 %). The models were obtained using MATLAB R2018b toolbox. In the developed neural models, the data obtained from process were used as neurons in the input ...
2020
The anaerobic digestion process is a technology that recovers energy in form of biogas and nutrients from biodegradable waste streams in useable forms in the absence of oxygen. It is sustainable, renewable and a zero-carbon form of energy supply. In this research work, modelling of biogas yield from co-digestion of food waste and animal manure using artificial neural networks was carried out. An experimental three stage continuous anaerobic digestion plant was used to co-digest food waste and animal manure. The composition of food waste and animal manure used include fufu, eba, starch, rice, beans, yam, fish, meat, moi moi, pig and cow dung. The feedstock was ground into fine particles to increase its surface area, and then mixed with water in a ratio of 1:2. The actual biogas yield was compared to the predicted biogas yield using artificial neural networks model. The performance of the developed artificial neural networks model was validated, and the results obtained from the resea...
Water Research, 1999
AbstractÐThe performance of three black box models which were parameterized and validated using data collected from a laboratory scale¯uidized bed anaerobic digester, were compared. The models investigated were all ARX (auto regressive with exogenous input) models, the ®rst being a linear single input single output (SISO) model, the second a linear multi-input multi-output (MIMO) model and the third a nonlinear neural network based model. The performance of the models were compared using correlation analysis of the residuals (one-step-ahead prediction errors) and it was found that the SISO model was the least able to predict the changes in the reactor parameters (bicarbonate alkalinity, gas production rate and % carbon dioxide). The MIMO and neural models both performed reasonably well. Though the neural model was shown to be superior overall to the MIMO model, the simplicity of the latter should be a consideration in choosing between them. A simulation with an horizon approaching 48 h was performed using this model and showed that although the absolute values diered signi®cantly, there were encouraging similarities between the dynamic behavior of the model and that of thē uidized bed reactor. #
Renewable and Sustainable Energy Reviews , 2020
An anaerobic fermentation process for biogas production integrated with wastewater purification in a modern wastewater treatment plant (WWTP) of designed nominal capacity 27,000 m 3 /day was modelled using artificial neural networks (ANNs). Neural models were trained, validated, and tested based on real-scale industrial data (covering three years of continuous plant operation), considering both technological aspects of the process and treated wastewater quality. An innovative approach addressing the simultaneous effect of seven adjustable main plant operation parameters together with wastewater characteristics (five parameters) on biogas production is reported for the first time in the literature. A parameter sensitivity analysis indicated clearly the higher importance of the operation process parameters on the biogas yield compared to the wastewater quality (COD, BOD 5 , TSS, P g , N g). The operation process parameters were the subject of modelling and analysis in respect to new, innovative possibilities, and technological strategies for biogas yield enhancement. The ANN model presented can be used as a predictive tool, an important element in such complex processes as steering/control strategies or for their optimisation procedures, as well as in the testing of other promising process intensification and optimisation scenarios.
Anaerobic digestion models for control purposes: a short survey
2015
In the literature one can find two important types of models: (i) ADM1-like models, mostly dedicated to knowledge integration to come up with a real virtual anaerobic digestion (AD) process and (ii) simplified models of the AD including a limited number of steps, mostly dedicated to process optimization and control. In this paper, we go through these two types of models by presenting first, a state of the art of the ADM1 in terms of applications and modifications and then some examples of simple models of AD. We claim that the interest of complex ADM1-like models can be relevant only if their emergent properties are analyzed. A way to do so is to study simpler models and to infer what their properties imply to the so-called “complex” models.
The Global Environmental Engineers, 2014
The processes of anaerobic digestion and co-digestion are complicated and the development of computational models that are capable of simulation and prediction of anaerobic digester performances can assist in the operation of the anaerobic digestion processes and the optimization for methane production. The artificial neural network approach is considered to be an appropriate and uncomplicated modelling approach for anaerobic digestion applications. This study developed neural network models to predict the outcomes of anaerobic co-digestion of leachate with pineapple peel using experimental data. The multilayered feed forward neural network model proposed was capable of predicting the outcomes of biogas production from the anaerobic co-digestion processes with a mean squared error for validation of 2.67 x 10-2 and a R value for validation of 0.9942. The approach was found to be effective, flexible and versatile in coping with the non-linear relationships using available information.
Aibi revista de investigación, administración e ingeniería, 2023
The present work was carried out with the objective of presenting the current importance of work related to the optimization of anaerobic digesters using artificial neural networks. A scientometric methodological approach was used for a systematic review of the publications indexed in Scopus until 2023. The H index was used to evaluate the visibility and impact of the journals, authors, countries, and institutions with the highest levels of production and recognition in the field of study. This review also allowed us to analyze the interaction between groups and knowledge networks with the authors and institutions identified in the classification. The results show a significant increase in the number of publications between the years 1973 and 2023, which allow us to characterize on a scale the main dimensions of research, development and innovation related to the study of optimization methods of anaerobic biodigesters for the production of biogas from different waste such as that from the palm oil extraction process. The results show a significant increase in the number of publications between 2016 and 2023, a total of 2847 documents were found, where 95.64% are in English. The country that presents the most publications on the topic is China with a contribution of 19.28%, followed by the United States with 9.8%, India with 7.2% and Spain with 6.2% among others.