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Online Monitoring of Volatile Fatty Acids in Biogas Plants via Data-Driven Software Sensors

International Journal of Chemical Engineering Research
© 2024 by SSRG - IJCER Journal
Volume 11 Issue 1
Year of Publication : 2024
Authors : Gerardo Lara-Cisneros
10.14445/23945370/IJCER-V11I1P101
pdf
How to Cite?

Gerardo Lara-Cisneros, "Online Monitoring of Volatile Fatty Acids in Biogas Plants via Data-Driven Software Sensors," SSRG International Journal of Chemical Engineering Research, vol. 11,  no. 1, pp. 1-9, 2024. Crossref, https://doi.org/10.14445/23945370/IJCER-V11I1P101

Abstract:

The Volatile Fatty Acids concentration (VFAs) is a critical component for operating and controlling biogas plants for biomethane production. However, the online monitoring sensors for VFAs are too expensive and require high maintenance costs. This paper proposes data-driven software sensors that can estimate VFAs online from the available online sensor data in biogas plants. From online sensor signals for the temperature, pH, flow rates and biogas composition as inputs and VFAs concentration as the target variable, two approaches are developed: Principal Component Analysis with Nonlinear Support Vector Regression (PCA-NSVR) and a Long Short-Term Memory (LSTM) recurrent neural network. The data set was obtained from numerical simulation in the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2) that includes the IWA Anaerobic Digestion Model No. 1 (ADM1) with dynamic influent data and noisy sensor signals. The performance of both software sensors was evaluated via mean-root square error for the testing data set. The results show that the ability of the LSTM recurrent neural network to capture the sequential dynamics in the input data makes this approach more efficient for the online estimation of VFAs.

Keywords:

Biogas plants, Biomethane, Anaerobic Digestion, VFAs estimation, Recurrent neural networks, Support vector regression, Software sensors.

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