Recent advances in dynamic modeling and control studies of biomass gasification for production of hydrogen rich syngas

Hussain, Maharra; Ali, Omer; Raza, Nadeem; Zabiri, Haslinda; Ahmed, Ashfaq; Ali, Imtiaz

doi:10.1039/d3ra01219k

Cited by 10 publications

(4 citation statements)

References 104 publications

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“…Biomass composition varies with feedstock type, the general representation is CH n O m . wH 2 O, as shown in [6], [8].…”

Section: Gasification Zonesmentioning

confidence: 99%

“…𝐂𝐇 𝟒 + 𝐇 𝟐 𝐎 ⇌ 𝐂𝐎 + 𝟑𝐇 𝟐 (5) 𝐂 + 𝐇 𝟐 𝐎 ⇌ 𝐂𝐎 + 𝐇 𝟐 (6) 𝐂 + 𝐂𝐎 𝟐 ⇌ 𝟐𝐂𝐎 (7) The oxidation zone is the source of heat for the reactions. It is the zone where combustion takes place.…”

Section: Figure 1: Reaction Zones Of Gasifier Reactormentioning

confidence: 99%

“…Because the thermodynamic behaviour of the gasification process is not fully understood due to the complex and non-linear nature of the process; it is very difficult to accurately describe the system dynamics from an analytical point of view. This has restricted the control methods that can be applied to the system because conventional control methods such are proportional integral derivative (PID) control are not effective in the control of gasifier reactor [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Sliding mode control for gasifier reactor temperature control

Ajah,

Eke,

Okoye

et al. 2024

Nig. J. Tech.

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This paper presents a sliding mode control (SMC) design for gasifier reactor temperature control. Temperature control is a critical aspect of biomass gasification for quality syngas production. However, accurate modeling and effective control method are the major challenges of gasifier reactor temperature control. The gasification system processes are not yet well understood to model from the first principle due to the complex and nonlinear nature of the process. Also, conventional control methods such as proportional, integral, and derivative control have not been effective in controlling gasification systems. Hence, this work has used experimental data from a 500 kVA updraft gasifier reactor to develop a data-driven mathematical model. The system identification result predicts 86.36% goodness of fit on the model data and 88.26% on the validation data. Discrete time sliding mode control has been designed from the model and implemented in Simulink to investigate the performance of the control method on the gasifier. The result of the system time response shows that the controller effectively drives the temperature monotonically from 25°C to 700 °C in finite time (11.37 minutes). It also establishes a quasi-sliding motion with an ultimate band of ±1℃ for the remainder of the simulation time. Hence, the control technique guarantees optimal temperature control for any feedstock type which will result in higher conversion efficiency, more syngas yield, and improve syngas quality and durability of the gasifier reactor among others.

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“…Biomass composition varies with feedstock type, the general representation is CH n O m . wH 2 O, as shown in [6], [8].…”

Section: Gasification Zonesmentioning

confidence: 99%

Section: Figure 1: Reaction Zones Of Gasifier Reactormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sliding mode control for gasifier reactor temperature control

Ajah,

Eke,

Okoye

et al. 2024

Nig. J. Tech.

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“…Similarly, Karout et al [25] explored a predictive controller for biomass gasification in a solar thermochemical reactor. While the efficiency of machine learning model-based controllers is a developing field, individual efforts are needed for specific applications, such as fluidized bed gasifiers, where dedicated studies are lacking [26].…”

Section: Introductionmentioning

confidence: 99%

Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

Faridi,

Tsotsas,

Kharaghani

2024

Processes

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This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation.

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