Abstract
Anaerobic digestion is associated with various crucial variables, such as biogas yield, chemical oxygen demand, and volatile fatty acid concentration. Real-time monitoring of these variables can not only reflect the process of anaerobic digestion directly but also accelerate the efficiency of resource conversion and improve the stability of the reaction process. However, the current real-time monitoring equipment on the market cannot be widely used in the industrial production process due to its defects such as expensive equipment, low accuracy, and lagging analysis. Therefore, it is essential to conduct soft sensor modeling for unmeasurable variables and use auxiliary variables to realize real-time monitoring, optimization, and control of the an-aerobic digestion process. In this paper, the basic principle and process flow of anaerobic digestion are first briefly introduced. Subsequently, the development history of the traditional soft sensor is systematically reviewed, the latest development of soft sensors was detailed, and the obstacles of the soft sensor in the industrial production process are discussed. Finally, the future development trend of deep learning in soft sensors is deeply discussed, and future research directions are provided.
Highlights
Anaerobic digestion is a highly complex biochemical reactions process, with characteristics such as multi-factor influence, dynamic change, and complex nonlinearity [1].Anaerobic digestion can treat organic pollutants and produce clean energy [2].anaerobic digestion technology has broad development space in the treatment of wastewater and organic solid waste [3] and is one of the practical ways to solve energy and environmental problems
The soft sensor of anaerobic digestion based on regression analysis majorly includes soft sensors based on multiple linear regression (MLR) and soft sensors based on partial least squares regression (PLSR)
Given the small-sample problem caused by the difficulty of obtaining target variables in the anaerobic digestion process, Kazemi proposed the soft sensor based on support-vector regression (SVR) to predict the volatile fatty acids (VFA) concentration [89]
Summary
Anaerobic digestion is a highly complex biochemical reactions process, with characteristics such as multi-factor influence, dynamic change, and complex nonlinearity [1]. The unexpected changes in the external environment have an impact on the hydrolysis, acidification, and methanation processes of anaerobic digestion [4,5] This will cause numerous volatile fatty acids (VFA) to accumulate in the reactor, inhibit the progress of methanation, and even result the failure of the anaerobic reactor operation [6,7,8]. A more advanced online measurement system must be used to fully monitor the anaerobic digestion process in real-time to ensure that the anaerobic digestion process is stable and efficient while obtaining a higher biogas yield [9]. The soft sensor using online measurable auxiliary variables to estimate the unmeasurable variables in real-time has been broadly used in the anaerobic digestion process [17,18]. The latest development of soft sensors is detailed, including the application of deep learning in the anaerobic digestion process. The future development trend of deep learning in soft sensors is deeply analyzed, and a summary and outlook are drawn
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