Abstract
Despite the importance of the biodegradability of lignocellulose biomass, few studies have evaluated the lignocellulose biomass digestion kinetics and modeling of the process. Anaerobic digestion (AD) is a mature energy production technique in which lignocellulose biomass is converted into biogas. However, using different organic waste fractions in AD plants is challenging. In this study, lignocellulose biomass (corn stover hydrochar) obtained from hydrothermal carbonization at a temperature, residential time, and biomass/water ratio of 215 °C, 45 min, and 0.115, respectively, was added to the bioreactor as a substrate inoculated with food waste and cow dung to generate biogas. A state–space AD model containing one algebraic equation and two differential equations was constructed. All the parameters used in the model were dependent on the AD process conditions. An adaptive identifier system was developed to automatically estimate parameter values from input and output data. This made it possible to operate the system under different conditions. Daily cumulative biogas production was predicted using the model, and goodness-of-fit analysis indicated that the predicted biogas production values had accuracies of >90% during both model construction and validation. Future work will focus on the application of modeling predictive control into an AD system that would comprise both models and parameters estimation.
Highlights
As the consumption of energy increases globally, fossil fuel resources decrease from overexploitation and are likely to become scarce or exhausted in future generations, developing renewable energy and alternative fuels is a promising solution to this situation [1,2]
To increase the supply of renewable energy, which is the key factor in the attainment of sustainable development goals, using lignocellulose biomass, which is not affected by environmental change, as a renewable and sustainable resource is necessary [5]
The aim of this study is to develop a state–space model appropriate for controlling biogas generation and an adaptive identifier that can automatically estimate the key parameters representing the input and output characteristics of the Anaerobic digestion (AD) process from experimental data
Summary
As the consumption of energy increases globally, fossil fuel resources decrease from overexploitation and are likely to become scarce or exhausted in future generations, developing renewable energy and alternative fuels is a promising solution to this situation [1,2]. To reduce the overdependence on fossil fuel consumption and pollutant emissions, exploitation of renewable energy resources like wind and solar power systems is required [4]. Environmental conditions affect the amount of renewable energy produced by wind and solar power systems, which are currently the main systems generating renewable energy around the world [5,6]. These systems are referred to as variable renewable resources. The production of lignocellulosic biomass is estimated to be 200 billion tons annually. Improper management of these lignocellulose biomass resources can pollute the environment [7–9]. A system in which energy is generated from lignocellulose biomass would be robust and could compensate for fluctuations
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