IPCS: An integrated process control system for enhanced in-situ bioremediation
Introduction
Enhanced in-situ bioremediation is an approach for cleaning up petroleum-contaminated sites (Schoefs et al., 2004). Enhanced in-situ biodegradation is used in conjunction with groundwater pumping and soil flushing systems to circulate aerated and nutrient-enriched water through a contaminated aquifer and associated soils through a series of injection wells. The process usually involves stimulating the indigenous bacterial with appropriate nutrients or introducing non-indigenous bacterial to degrade the organic contaminants in groundwater (Huang et al., 2007). Generally, to improve remediation efficiency and cost-effectiveness, many factors, such as pumping rate, oxygen addition, nutrient supply, and groundwater temperature, can be adjusted to reach the desired levels for microorganisms to degrade the hydrocarbon effectively (Lenczewski et al., 2003). The designed system must be able to facilitate on-line adjustment and control of these factors according to varying site conditions (Rutherford and Johnson, 1996). Therefore, an integrated process control approach for enhanced in-situ bioremediation system is desired to make these systems more efficient.
Previous approaches of model predictive control (MPC) and intelligent process control contributed to improving product quality and efficiency and reducing the costs of industrial production and pollution control (Khalid, 1993, Tan, 1996, Ferrer et al., 1998, Rao and Rawlings, 2000, Zhou et al., 2002, Guh, 2003). MPC is capable of dealing with linear and simple non-linear systems. However, for non-linear predictive control of a subsurface remediation system, the optimization problem that needs to be solved on-line may require tremendous computational efforts, and in many cases this cannot be completed even by fast computing systems. Although intelligent process control does not have to deal with complex non-linear issues, it has a high requirement of prior operation knowledge that highly depends on expert experiences and may not be available for practical in-situ bioremediation (Huang et al., 2003a). It is desirable to develop a process control system for site remediation practices using a more effective simulation-optimization hybrid by capitalizing the advantages of both model predictive control and intelligent process control.
As an extension of previous efforts, Huang et al. (2006a) developed an integrated numerical and physical modeling system for simulating an enhanced in-situ biodegradation process coupled with three-dimensional multiphase multicomponent flow and transport within a multi-dimensional pilot-scale physical model. With the developed integrated modeling system. A forecasting system was then developed for optimal remediation design and process control based on stepwise-cluster analysis (Huang et al., 2006b). In this paper, based on Huang et al., 2006a, Huang et al., 2006b, a framework will be proposed to show how an integrated process-control system (IPCS) can be developed for a complex subsurface remediation system, based on techniques of physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks (ANN). The development of the optimization model and the ANN controller will be emphasized for the bioremediation process within a pilot-scale system, targeting on realizing integrated process control. A complete optimization model will be developed, with a set of optimization results being generated through the genetic algorithm. Then an artificial neural networks (ANN) model will be developed to establish the relationship between various remediation conditions and responding benzene concentrations. Finally, a framework of process control system will be developed.
Section snippets
Research framework
Fig. 1 shows the general procedure for developing a process control system for enhanced in-situ bioremediation. It consists of seven steps. First, an integrated numerical and physical modeling system is developed through designing a three-dimensional (3-D) pilot-scale model to support the operation of enhanced in-situ bioremediation after a hydrocarbon spill, and developing a multi-phase, multi-component subsurface flow and transport model with biodegradation kinetics that reflects the in-situ
Pilot-scale model
A physical pilot-scale system was developed to support calibration and verification of numerical models with interior dimensions of 3.6 meters (m), 1.2 m, and 1.4 m for the length (L), width (W), and height (H), respectively. Stratified soil in the system contained four layers. In total, 25 wells were installed in the system for monitoring purpose. Some experimental design factors are listed in Table 1. Detailed design of the pilot-scale system is presented in Huang et al. (2006a). Tap water in a
Development of a forecasting system
A forecasting system based on stepwise cluster analysis (SCA) was then developed to reflect the relationships between system performance and operating condition. A stepwise cluster analysis method can effectively deal with discrete and non-linear complexities (Huang, 1992). The solutions of the numerical model (benzene concentrations at selected locations, denoted as , where ) were considered as dependent variables in the stepwise cluster analysis. The operation conditions were
Non-linear optimization through genetic algorithm
For each contamination-level scenario (with the initial benzene concentrations of (), the resulting forecasting system could be directly incorporated into a non-linear optimization model as follows:subject to:where is the operating-conditions variable, ; is
Neural networks for supporting process control
During past years, significant progress in the fields of non-linear dynamic pattern recognition and system control theory was made through advances of artificial neural network modeling (ANN). ANN is a non-linear and dynamic mathematical structure with a unique ability of recognizing underlying relationships between input and output events (Hsu et al., 1995). ANN is composed of many simple interconnected neurons which have two components (Dayhoff, 1990): (1) a weighted sum which performs a
Process control system for in-situ bioremediation
The developed NN were trained to establish relationships between optimal operation decisions and subsurface-contamination situations. Therefore, it could be used as a process controller for operating the remediation system. As the last two steps in Fig. 1, a simple control system for enhanced in-situ bioremediation was designed (Fig. 4). First, benzene concentrations at the selected wells at the beginning of time period were monitored. The concentration levels were then used as inputs for the
Conclusions
A framework was proposed to develop an integrated process-control system (IPCS) for a complex subsurface remediation system, based on techniques of physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks (ANN). Process control for enhanced in-situ biodegradation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. The development of the optimization
Acknowledgements
The authors would like to thank the anonymous reviewers for their insightful comments and suggestions that were helpful for improving the manuscript. This paper was supported by the “National Basic Research (973) Program” Project (No. 2005CB724202) of the Ministry of Science and Technology of China, National Natural Science Foundation of China (No. 50509010 and No. 50221903), and Tsinghua University Basic Research Foundation (JCQN2005008).
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