Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R² of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R² values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.

Strategies for enhancing the efficacy of anaerobic digestion of food industry wastewater: An insight into bioreactor types, challenges, and future scope

Food waste have become a growing concern worldwide with raising population and economic growth. Wastewater discharged from food industries contains many valuable and toxic components that have a negative impact on the ecological system. Large amounts of wastewater are discharged from the food industry, which necessitates the creation of effective technologies. Wastewater from the food industry can be seen as a rich source of energy and a primary source for generating valuable products. Waste disposal and resource recovery are sustainably valued by anaerobic digestion of wastewater from the food sector. The characteristics, composition, and nature of wastewater produced from various food sectors are elaborated upon in this review. An overview of the anaerobic digestion process for wastewater treatment in the food industry is included. Enhancement strategies for the anaerobic digestion process have been discussed in detail. In addition, various types of reactors utilized for performing anaerobic digestion is illustrated. Though anaerobic digestion process possesses advantages, the challenges and future scope are examined for improving the outcome.

Hydrothermal and thermal-alkali pretreatments of wheat straw: Co-digestion, substrate solubilization, biogas yield and kinetic study

Agro-waste having lignocellulosic biomass is considered most effective (heating value 16 MJ/kg) for energy production through anaerobic digestion (AD). However, recalcitrant lignocellulosic fraction in agro-waste obstructs its biotransformation and is a rate-limiting step of the process. This study investigated the effects of hydrothermal and thermal-alkaline pretreatment on anaerobic co-digestion of wheat straw (WS). The hydrothermal pretreatment of WS revealed that 60 min was the best pretreatment time to achieve the highest substrate solubilization. It was employed for thermal-alkali pretreatment at variable temperatures and NaOH doses. Thermal-alkali pretreatment at 125°C-7% NaOH shows the highest (34%) biogas yield of 662 mL/gVS, followed by 646 mL/gVS biogas yield at 150°C-1% NaOH assay (31% higher) over control. Although the 125°C-7% NaOH assay achieved the highest biogas yield, the 150°C-1% NaOH assay was found more feasible considering the cost of a 6% higher chemical used in the earlier assay. The thermal-alkali pretreatment was observed to reduce the formation of recalcitrant compounds (HMF, Furfural) and increase the buffering capacity of the slurry over hydrothermal pretreatment. Principal component analysis (PCA) of the various pretreatment and AD operational parameters was carried out to study their in-depth correlation. Moreover, a kinetic study of the experimental data was performed to observe the biodegradation trend and compare it with the Modified Gompertz (MG) and First Order (FO) models.