Elsevier

Applied Soft Computing

Volume 11, Issue 4, June 2011, Pages 3812-3820
Applied Soft Computing

Adaptive dissolved oxygen control based on dynamic structure neural network

https://doi.org/10.1016/j.asoc.2011.02.014Get rights and content

Abstract

Activated sludge wastewater treatment processes (WWTPs) are difficult to control because of their complex nonlinear behavior. In this paper, an adaptive controller based on a dynamic structure neural network (ACDSNN) is proposed to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). The proposed ACDSNN incorporates a structure variable feedforward neural network (FNN), where the FNN can determine its structure on-line automatically. The structure of the FNN is adapted to cope with changes in the operating characteristics, while the weight parameters are updated to improve the accuracy of the controller. A particularly strong feature of this method is that the control accuracy can be maintained during adaptation, and therefore the control performance will not be degraded when the character of the model changes. The performance of the proposed ACDSNN is illustrated with numerical simulations and is compared with the fixed structure fuzzy and FNN approaches; it provides an effective solution to the control of the DO concentration in a WWTP.

Introduction

Water pollution is one of the most serious environmental problems due to the discharge of nutrients into receiving waters [1]. Hence, to improve the effectiveness of the treatment there is a need to implement better methods of control. However, the most popular treatment method used in the field of wastewater plants is biological [2]. In particular, processes based on activated sludge technology offer a very good solution for pollution removal in wastewater. Because of the complexity of the physical, chemical and biological phenomena associated with treatment units, the performance of the process is heavily dependent on environmental and operational conditions. Wastewater treatment processes (WWTPs) are difficult to control due to large disturbances in flow and load and also the different physical and biological phenomena which can take place. Several extensive surveys of the activated sludge process control using simulation can be found in the literature [3], [4].

Nowadays, dissolved oxygen (DO) level control is the most widely used method since oxygen is a key substrate in animal cell metabolism and its consumption can therefore be used to effectively monitor the whole process [5]. The DO level in the aerobic reactors has significant influence on the behavior and activity of the heterotrophic and autotrophic microorganisms living in the activated sludge. The DO concentration in the aerobic part of an activated sludge process should be sufficiently high to supply enough oxygen to the micro-organisms in the sludge so that organic matter is degraded and ammonium is converted to nitrate. On the other hand, however, an excessively high level of DO, which requires a high air inflow rate, leads to a high energy consumption and may result in a deterioration of the sludge quality. Experiments show that a high level of DO in the internally recirculated water also makes the denitrification less efficient. Hence, both for economic and processing reasons it is important to control the DO concentration.

A lot of research has been published about methods to control the DO in order to improve the process. The classical methods (on/off [6] and proportional-integral-derivative (PID) [7]) have largely been tried, but due to the nonlinear character of the bioprocesses and the lack of available models, the controllers were developed for specific operating and environmental conditions. More recently, researchers have started to employ artificial intelligence techniques, which can be found in a wide variety of application fields including chemicals [8], food processing [9], automation [10], and other complex nonlinear systems [11]. The most popular artificial intelligence techniques used to control DO concentration are fuzzy and neural networks. Ferrer et al. [12] studied a fuzzy approach to the DO control in the aeration process. The inputs to the fuzzy controller are the actual DO concentration, the value of the error, the error change and the accumulated error; the outputs are the air flow and its change. Compared with conventional on/off control, the fuzzy controller can save about 40% of the energy. Traore et al. [13] have presented a fuzzy logic strategy to control the DO level in a sequential batch reactor (SBR) pilot plant. The strategy was shown to be both robust and effective; it was also easy to integrate it into a global cost management monitoring system. However, despite the fact that the fuzzy approach has been successfully applied to many practical applications [14], [15], there is still no clear and easy design methodology. This is mainly because constructing a fuzzy control rule base is semi-empirical rather than learned. Consequently, a neural network, which is able to learn nonlinear functional relationships without the need for a structural knowledge of the process has become popular. Syu and Chen [16] have proposed an adaptive control for DO concentration in the sewage treatment process using a BP neural network. The control system designed set the minimum dose for performance indicators and the effluent chemical oxygen demand (COD) to meet the emission targets. Lee et al. [17] have developed an automatic control system using a neural network and internet-based remote monitoring system that increases the operating efficiency of plants that have a serious influent loading variance. The results show that regardless of loading variance, more than 95% of organic matters and more than 60% of nitrogen and phosphorus are removed. Huang et al. [18] proposed an integrated neural–fuzzy process controller to control aeration for DO concentration. It was shown that there is an operational cost saving of almost 30% when the fuzzy–neural controller is switched on. There are other approaches based on neural networks which have also been used to control DO concentration [19], [20]. However, it is difficult for designers and technical domain experts to estimate all the input-output data from such a complex system and thus determine the appropriate structure of the controller's neural network. Previous works on controllers have had limited success because they have not addressed the main issues associated with the complex nonlinear WWTP: a successful control scheme must address the dynamic nature of the problem and the different methods used by plants in different operating regions.

In this paper, an adaptive controller based on a dynamic structure neural network (ACDSNN) is proposed which is capable of adapting to the nonlinear dynamics of the WWTPs used in different operating regions. The dynamic structure neural network can adapt both the network structure (number of hidden units) and parameters (weights). This characteristic makes it ideal for complex non-linear dynamic applications [21], [22], [23]. It is reasonable to suppose that feedforward neural network (FNN) design algorithms with structure adaptive strategies will have better performance. In this paper a new structure adjusting strategy is developed that is applicable to both constructing as well as pruning. The novel full structure optimization is intended to optimize the entire FNN structure by means of an approach combining the error reparation (ER) with the activities of the hidden units. The proposed dynamic structure neural network can obtain a compact structural size on-line according to the characters of the WWTPs. The ACDSNN improves several key areas of the controller—its response, accuracy and robustness. This paper applies the ACDSNN to the WWTP and shows how it is able to improve the control of the DO concentration.

The outline of this paper is as follows: in Section 2, the activated sludge system model is described. Section 3 briefly discusses the FNN model and introduces the winning frequency function and redundant frequency function for structure design. Section 4 discusses the proposed ACDSNN. The experimental results of the simulations are presented in Section 5. The performance of the ACDSNN is compared with several other intelligent methods. The simulation results demonstrate that ACDSNN is a more effective controller: it can ensure that the water quality meets the expected level and has a smaller overshoot. Finally, Section 6 concludes the paper.

For convenience of discussion, Table 1 lists the acronyms used in this paper.

Section snippets

Activated sludge system model

The WWTP is a dynamic model. In order to simulate the processes that occur in a biological treatment process, the benchmark represents a continuous-flow pre-denitrifying activated sludge process which contains a reactor tank and a settling tank. The fundamentals of the activated sludge WWTP are shown in Fig. 1.

A mathematical model is obtained based on the following assumptions [24]:

  • (1)

    The microorganisms’ growth rate is larger than their death rate and obeys the Monod law [27].

  • (2)

    No biochemical

Feedforward neural network

Without loss of generality, a single hidden layer FNN is used in this paper. The structure of the FNN is shown in Fig. 2.

Bypass weights from the input layer to the output layer are used, but are not shown in Fig. 2 for clarity. As the process described above is a MISO system, the FNN is also a MISO. There are M units in the input layer, N units in the hidden layer, and one unit in the output layer.

The function and activation of each layer are:

Input layer: there are M units in this layer; each

Discussion of the adaptive controller based on a dynamic structure neural network (ACDSNN)

The main and most obvious control goal to be achieved at a wastewater treatment plant is, of course, to keep the plant running. The second one is to fulfill the effluent quality standards, while minimizing the operational costs. The wastewater treatment is a complex nonlinear system which is hard to model. This paper proposes using the ACDSNN to address these problems. It can realize adaptive control by changing the structure as well as parameters of the neural network when applied to activated

Experimental studies

To demonstrate the effectiveness of the proposed ACDSNN, the controller developed in this work is implemented in three cases. First, sudden step load increase and load reductions without disturbances are considered for this case. The constant DO concentration is required for the WWTP. The inflow rate Q and the wastage flow Qw is given in Fig. 5. In the Figures, the wastewater treatment plant works at a high rate from time 8:00 to 16:30, and works at a low rate at other times. Second, sudden

Discussion and conclusion

Three critical issues in the WWTP are its dynamic characteristic, different DO concentration requirements and influent disturbances. By using an adaptive controller based on dynamic neural network architecture, ACDSNN offers an efficient solution to the DO control problem when dealing with the wastewater treatment system. Specifically, its online architecture learning capability enables the process to adapt when the operating character of the WWTP changes.

Another benefit of using the proposed

Acknowledgements

The authors would like to thank Prof. Ralph for reading the manuscript and providing valuable comments. The authors also would like to thank the anonymous reviewers for their valuable comments and suggestions, which helped improve this paper greatly.

This work was supported by the National 863 Scheme Foundation of China under Grant 2009AA04Z155 and 2007AA04Z160, National Science Foundation of China under Grants 61034008 and 60873043, Ph.D. Program Foundation from Ministry of Chinese Education

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