Monitoring and control of the processes involved in the capture and filtering of biogas using FPGA embedded fuzzy logic

Abstract

This article focuses on the development of an intelligent control platform capable of improving the capture and filtration of biogas with the aim of raising the calorific value of the fuel. The biogas was produced in an upflow anaerobic sludge blanket (UASB) reactor at the sewage treatment plant (STP) located near to the town of Los Angeles, Campo Grande, Mato Grosso do Sul, Brazil. For the purposes of system identification, the concentrations, flow rates and pressures of the gases were continuously monitored before and after the filtering processes. The fuzzy proportional derivative controller described herein was required to sustain maximum biogas flow whilst ensuring that the concentration of oxygen did not exceed a predetermined point (the SetPoint) regardless of the variation in load received by the digester. The controller input was oxygen concentration, and the processed output signal was sent to an inverter responsible for raising or lowering the speed of the compressor used in the capture of the biogas. The controller design was developed using MATLAB software and implemented in a fieldprogrammable gate array (FPGA)-embedded Nios II processor. The embedded control algorithm was validated with the aid of ModelSim software. Electronic circuits were developed to allow the FPGA board and the sensors and actuators to communicate via protocols widely used in the industrial setting. This design factor, taken together with the incorporation of reconfigurable hardware and the facility of changing the rules of the fuzzy controller, afforded maximum flexibility of the control platform for use in other applications.

Economic viability of the biogas produced on pig farms in Brazil for electric power generation

“…Highlighted also, in agreement with Vanti et al (2015), is that raw biogas has a strong odor, low heat potential and is highly corrosive, which means that it is not indicated for internal combustion engines, thus there arises the need for purification processes to be applied to the raw product in order to elevate its heat potential and remove components that are responsible for corrosion and the bad odor.…”

Integration of Artificial Intelligence into Biogas Plant Operation

“…There have also been machine learning applications in the natural sciences, that also enable intelligent decision-making systems, and artificial and computational intelligence in the world of data science. Daily time series data in a wastewater treatment plant were used as input for developing a support vector machine model by Manu and Thalla (2017), and it was observed that is possible to define the relation between dependent and independent variables with help of machine learning algorithms [135,136].…”

A Deep Learning Approach to Optimize Recombinant Protein Production in Escherichia coli Fermentations

“…Major contributions by ML approaches have increased the speed of process data analysis while reducing the number of experiments required and the number of independent variables to be monitored, eventually allowing for the identification of optimal process conditions. In this context, machine learning methods have been investigated for process optimization in biogas productions [7,8] and wastewater treatments [9,10]. Only more recently have a few attempts at modeling fermentation processes with machine learning been explored [11,12].…”