Integration of Artificial Intelligence into Biogas Plant Operation

Cinar, Samet; Cinar, Senem Önen; Wieczorek, Nils; Sohoo, Ihsanullah; Kuchta, Kerstin

doi:10.3390/pr9010085

Cited by 41 publications

(16 citation statements)

References 118 publications

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“…Te prominent energy sources are fossil fuels such as petroleum, diesel, kerosene, and liquefed natural gas, among others [1]. Te application of fossil fuels is accompanied by various environmental pollution, such as the greenhouse gas efect, depletion of the ozone layer, ecological challenges, and climate disturbance, among others [2,3]. It is in this connection that the need arises for other origins of alternative energy, which is capable of engineering sustainable development [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

Fajobi

Lasode

Adeleke

et al. 2023

Journal of Engineering

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One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.

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Section: Introductionmentioning

confidence: 99%

Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

Fajobi

Lasode

Adeleke

et al. 2023

Journal of Engineering

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“…The anaerobic digestion process has four sequential stages: hydrolysis, acidogenesis, acetogenesis and methanogenesis. Since there are different kinds of microorganisms taking part in each stage of the anaerobic digestion process, supplying a suitable environment for a sustainable process is complex and makes it challenging monitor and control the process [3]. Any kind of anomaly from one of these stages can cause an unstable process.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Cinar

Kuchta

2022

Fermentation

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Process optimization is no longer an option for processes, but an obligation to survive in the market in any industry. This argument also applies to anaerobic digestion in biogas plants. The contribution of biogas plants to renewable energy can be increased through more productive systems with less waste, which brings the common goal of minimizing costs and maximizing yields in processes. With the help of data science and predictive analytics, it is possible to take conventional process optimization and operational excellence methods, such as statistical process control and Six Sigma, to the next level. The more advanced the process optimization aspect, the more transparent and responsive the systems. In this study, seven different machine learning algorithms—linear regression, logistic regression, K-NN, decision trees, random forest, support vector machine (SVM) and XGBoost—were compared with laboratory results to define and predict the possible impacts of wide range temperature fluctuations on process stability. SVM provided the best accuracy with 0.93 according to the metric precision of the models calculated using the confusion matrix.

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“…With the harmonization of these parameters, it is possible to maintain an optimum environment for biological processes such as biogas production. To illustrate this, characteristics of the feedstock-such as composition, ratio of Total Organic Carbon (TOC) and Total Nitrogen (TN), particle size, total solids-as well as parameters during the process-including temperature, Organic Loading Rate (OLR), Hydraulic Retention Time (HRT), mixing, and pH-could be considered some of the operational parameters that affect the process capability [5][6][7][8][9][10][11]. Recent research has shown that temperature has a major effect on biogas production.…”

Section: Introductionmentioning

confidence: 99%

“…Use of statistics in any field with the help of Lean Six Sigma can lead to the reaping of the benefits of modern methods such as machine learning, predictive analytics, and big data. In this study, extensive literature research has been done for this topic [11,35]. For instance, De Clercq et al developed a machine learning model to predict biogas production and created a graphical user interface with the help of operational data from two industrial scale biogas plants in China [36].…”

Section: Introductionmentioning

confidence: 99%

Operational Excellence in a Biogas Plant through Integration of Lean Six Sigma Methodology

Cinar

Staudter³

et al. 2022

Designs

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Process optimization with Lean Six Sigma (LSS) has become more popular every day for years in almost every kind of industry. This integration has brought an even wider variety of possible application areas for industries and research institutes. Recently, the use of LSS for process optimization in biological fields has become more and more common. In this study, LSS methodology is used for process optimization in an industrial scale biogas plant in Hamburg, Germany. The methodology used includes all the DMAIC cycle and related tools. Hypothesis tests were used to calculate the p-value of each experiment for the LSS interpretation. Due to the experimental factors, one-way ANOVA and 1-sample Z-test were used to determine the p-values. By conducting hypothesis testing after the analysis phase of this study, it was found that particle size, freshness of the substrate, and the amount of sand content in the substrate had a significant effect on the desired amount of biogas produced with a p-value of less than 0.01. These root causes led to approaches that focused on high quality feedstock and sufficient pretreatment methods. This paper represents a pioneering example of integrating Lean Six Sigma into biogas plant operation.

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Integration of Artificial Intelligence into Biogas Plant Operation

Cited by 41 publications

References 118 publications

Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Operational Excellence in a Biogas Plant through Integration of Lean Six Sigma Methodology

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