Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
One free energy source that is sustainable and renewable is the hydrogen gas. The most interesting fact about this gas is when used on vehicles or fuel cells, it is more efficient than the conventional internal combustion engines.When these compounds react with air, the by-product is just water. Be...
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my.um.stud.74322020-01-29T22:40:16Z Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya Azwar, Muhammad Yahya TA Engineering (General). Civil engineering (General) TP Chemical technology One free energy source that is sustainable and renewable is the hydrogen gas. The most interesting fact about this gas is when used on vehicles or fuel cells, it is more efficient than the conventional internal combustion engines.When these compounds react with air, the by-product is just water. Because of these advantages, the hydrogen gas is known as a low-carbon energy source. This is one solution for modern society, so that the dependence on fossil fuels can be minimized. Other advantage of the hydrogen gas is that it can be produced from various surrounding sources such as water, natural gas, biomass and organic waste. When viewed in terms of the economy, the selection of hydrogen fuel as a source of alternative energy is very appropriate. There are several alternative technologies for the production of biohydrogen process namely biophotolysis, photofermentation, dark fermentation and microbial electrolysis cells (MEC). However, MEC is known to be an attractive alternative technology that is environmentally friendly and can be used for hydrogen production. It involves a bioelectrochemical process using microorganisms as catalysts. MEC process has advantages compared with other processes because the microorganisms are capable of oxidizing all organic substrates to produce hydrogen gas which cannot be extracted by microorganisms through the photo or dark fermentation process. Microorganisms in the reactor are able to catalyze the oxidation or reduction reaction at the anode and cathode electrodes, respectively. By increasing the cathode potential in the MEC reactor, it is possible to continuously produce hydrogen electron exchange assisted by the bacteria. This method greatly decreases the amount of energy needed to produce hydrogen from organic matter compared to hydrogen production from water via electrolysis. Hydrogen production process in the MEC is a highly nonlinear and complex due to the microbial interactions. Its complexity makes MEC system difficult to operate and control under optimal conditions. However, these problems can be alleviated using an integrated process system engineering approach, which involves process modeling, optimization and control complementing each other. Artificial Neural Networks (ANN) is one of the most effective and powerful technique to be used to model such complex processes and unknown systems. ANN is able to cope with non-linear process between input and output variables without the requirement of explicit mathematical representation. In the process control system, ANN has been widely used when conventional control techniques failed to give good performance. In this work, various schemes including ANN for controlling the current and voltage of MEC were studied i.e. Direct Inverse Neural Network, Hybrid PID-Neural Network and Internal Model-based Control schemes. A comparative study has been carried out under optimal condition for the production of hydrogen gas where the controller output are based on the correlation of optimal current and voltage to the MEC system. Various simulation studies involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. On-line model validation of MEC system and closed loop control for online system are also presented in this work. 2017 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/7432/1/All.pdf application/pdf http://studentsrepo.um.edu.my/7432/6/azwar.pdf Azwar, Muhammad Yahya (2017) Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya. PhD thesis, University of Malaya. http://studentsrepo.um.edu.my/7432/ |
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TA Engineering (General). Civil engineering (General) TP Chemical technology Azwar, Muhammad Yahya Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya |
| description |
One free energy source that is sustainable and renewable is the hydrogen gas. The most interesting fact about this gas is when used on vehicles or fuel cells, it is more efficient
than the conventional internal combustion engines.When these compounds react with air, the by-product is just water. Because of these advantages, the hydrogen gas is known as
a low-carbon energy source. This is one solution for modern society, so that the dependence on fossil fuels can be minimized. Other advantage of the hydrogen gas is that
it can be produced from various surrounding sources such as water, natural gas, biomass and organic waste. When viewed in terms of the economy, the selection of hydrogen fuel
as a source of alternative energy is very appropriate.
There are several alternative technologies for the production of biohydrogen process namely biophotolysis, photofermentation, dark fermentation and microbial electrolysis cells (MEC). However, MEC is known to be an attractive alternative technology that is environmentally friendly and can be used for hydrogen production. It involves a bioelectrochemical process using microorganisms as catalysts. MEC process has advantages compared with other processes because the microorganisms are capable of oxidizing all organic substrates to produce hydrogen gas which cannot be extracted by microorganisms through the photo or dark fermentation process. Microorganisms in the reactor are able to catalyze the oxidation or reduction reaction at the anode and cathode electrodes, respectively. By increasing the cathode potential in the MEC reactor, it is possible to continuously produce hydrogen electron exchange assisted by the bacteria. This method greatly decreases the amount of energy needed to produce hydrogen from organic matter compared to hydrogen production from water via electrolysis. Hydrogen production process in the MEC is a highly nonlinear and complex due to the microbial interactions. Its complexity makes MEC system difficult to operate and control under optimal conditions. However, these problems can be alleviated using an integrated process system engineering approach, which involves process modeling, optimization and control complementing each other. Artificial Neural Networks (ANN) is one of the most effective and powerful technique to be used to model such complex processes and unknown systems. ANN is able to cope with non-linear process between input and output variables without the requirement of explicit mathematical representation. In the process control system, ANN has been widely used when conventional control techniques failed to give good performance. In this work, various schemes including ANN for controlling the current and voltage of MEC were studied i.e. Direct Inverse Neural Network, Hybrid PID-Neural Network and Internal Model-based Control schemes. A comparative study has been carried out under optimal condition for the production of hydrogen gas where the controller output are based on the correlation of optimal current and voltage to the MEC system. Various simulation studies involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. On-line model validation of MEC system and closed loop control for online system are also presented in this work. |
| format |
Thesis |
| author |
Azwar, Muhammad Yahya |
| author_facet |
Azwar, Muhammad Yahya |
| author_sort |
Azwar, Muhammad Yahya |
| title |
Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
|
| title_short |
Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
|
| title_full |
Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
|
| title_fullStr |
Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
|
| title_full_unstemmed |
Modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / Azwar Muhammad Yahya
|
| title_sort |
modelling and on-line implementation of advanced control strategies for biohydrogen production in microbial electrolysis cell reactor system / azwar muhammad yahya |
| publishDate |
2017 |
| url |
http://studentsrepo.um.edu.my/7432/1/All.pdf http://studentsrepo.um.edu.my/7432/6/azwar.pdf http://studentsrepo.um.edu.my/7432/ |
| _version_ |
1738506020099981312 |
| score |
13.211869 |