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In this paper, an intelligent control scheme for a class of anaerobic digester, for wastewater treatment, is proposed. The control scheme results from the combination of nonlinear output feedback control and a fuzzy-based gain scheduling scheme. Such a controller achieves the substrate regulation in the face of modeling errors (which are mainly rel...
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... the present section, an intelligent control scheme is derived from the combination of geometric control and fuzzy logic techniques. The block diagram of the proposed approach is presented in Figure 1. ...
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... the present section, an intelligent control scheme is derived from the combination of geometric control and fuzzy logic techniques. The block diagram of the proposed approach is presented in Figure 1. In previous works, it has been showed that system (1) satisfies the following geometric properties [8], ...
Citations
This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.
This chapter deals with the application of Higher Order Neural Networks (HONN) on the modeling and simulation of two processes commonly used to produce gas with energy potential: anaerobic digestion and gasification. Two control strategies for anaerobic digestion are proposed in order to obtain high biomethane flow rate from degradation of organic wastes such as wastewater. A neurofuzzy scheme which is composed by a neural observer, a fuzzy supervisor, and two control actions is presented first. After that, a speed-gradient inverse optimal neural control for trajectory tracking is designed and applied to an anaerobic digestion model. The control law calculates dilution rate and bicarbonate in order to track a methane production reference trajectory under controlled conditions and avoid washout. A nonlinear discrete-time neural observer (RHONO) for unknown nonlinear systems in presence of external disturbances and parameter uncertainties is used to estimate the biomass concentration, substrate degradation, and inorganic carbon. On the other side, a high order neural network structure is developed for the process identification in a gasification reactor; the gas, composed mainly of hydrogen and carbon monoxide (synthesis gas or syngas), is produced from thermo chemical transformation of solid organic wastes. The identifier is developed in order to reproduce a kinetic model of a biomass gasifier. In both cases (biological and thermo chemical processes), the Extended Kalman Filter (EKF) is used as a training algorithm. The proposed methodologies application is illustrated via numerical simulations.
In anaerobic co-digestion plants a mix of organic materials is converted to biogas using the anaerobic digestion process. These organic materials, called substrates, can be crops, sludge, manure, organic wastes and many more. They are fed on a daily basis and significantly affect the biogas production process. In this thesis dynamic real-time optimization of the substrate feed for anaerobic co-digestion plants is developed. In dynamic real-time optimization a dynamic simulation model is used to predict the future performance of the controlled plant. Therefore, a complex simulation model for biogas plants is developed, which uses the famous Anaerobic Digestion Model No. 1 (ADM1). With this model the future economics as well as stability can be calculated resulting in a multi-objective performance criterion. Using multi-objective nonlinear model predictive control (NMPC) the model predictions are used to find the optimal substrate feed for the biogas plant. Therefore, NMPC solves an optimization problem over a moving horizon and applies the optimal substrate feed to the plant for a short while before recalculating the new optimal solution. The multi-objective optimization problem is solved using state-of-the-art methods such as SMS-EMOA and SMS-EGO. The performance of the proposed approach is validated in a detailed simulation study.
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This paper deals with the design and validation via numerical simulations of an adaptive proportional integral (PI) controller for bicarbonate regulation in an anaerobic wastewater treatment process. The proportional and integral gains are deduced as a function of the parameters of an approximate dynamical model. First, a brief explanation of the control structure is introduced. After that, a general equation of an adaptive PI controller is proposed. Next, the controller is implemented in order to regulate bicarbonate by two control actions in a completely stirred tank reactor (CSTR): adding a base and manipulating the dilution rate. Finally, a smooth switching mechanism which allows the process to operate in open or closed loop according to the operating conditions is implemented. The performance of the process with the proposed control scheme is validated via simulations. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
This paper presents the dynamic behaviour of the anaerobic digestion process, based on a simplified model. The hydraulic,
biological and physicochemical processes such as those which underpin anaerobic digestion have more than one stable stationary
solution and they compete with each other. Further, the attractive domains of the stable solutions vary with the key parameters.
Thus, some initial transient process moving toward one stable solution could suddenly move towards another solution, at which
a so-call catastrophe takes places (e.g. washout). The paper systematically analyses the stationary solutions with their associated
stability, which provides insight and guidance for anaerobic digestion reactor design, operation and control.
