Development of an expert system for the remediation of petroleum-contaminated sites

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1 Environmental Modeling and Assessment 8: , Kluwer Academic Publishers. Printed in the Netherlands. Development of an expert system for the remediation of petroleum-contaminated sites Z. Chen a, G.H. Huang a,c,c.w.chan b,l.q.geng b and J. Xia c a Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 gordon.huang@uregina.ca b Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 c Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing, China Groundwater and soil contamination caused by light nonaqueous phase liquids (LNAPLs) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies, generally classified as ex situ and in situ remediation techniques, have been developed and implemented to clean up the contaminated sites in the last two decades. One of the problems associated with ex situ remediation is the cost of operation. In recent years, in situ techniques have acquired popularity. However, the selection process of the desired techniques needs a large amount of knowledge. Insufficient expertise in the process may result in unnecessary inflation of expenses. In this study, petroleum waste management experts and Artificial Intelligence (AI) researchers worked together to develop an expert system (ES) for the management of petroleum contaminated sites. Various AI techniques were used to construct a useful tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation in the project. The expert system was applied to a real-world case study and the results show that the expert system can generate desired remediation alternatives to assist decision-makers. The application case study constitutes partial validation of the prototype expert system. Keywords: petroleum waste, soil, groundwater, remediation, artificial intelligence, expert system, contamination 1. Introduction Petroleum industry is one of the major economic sectors in North America. Recently, development of the petroleum industry is associated with a number of environmental concerns. Among them, soil and groundwater contamination resulting from leakage of storage tanks, pipelines, dispenser pumps, and overfilling has generated growing concerns [8,14,15]. Adams et al. [1] estimated that there were 1,800,000 understorage tanks (USTs) in United States, of which about 1,200,000 contain petroleum (hydrocarbon) products. Tejada [16] reported that at least 23% of all the storage tanks leak. The main causes of the leakage are corrosion (for steel USTs) and breakage (for fiberglass USTs) [7,16]. Local government and the related industries need to address clean-up of contaminated soil and groundwater, and innovative artificial intelligence (AI) techniques can provide the support for the decision processes involved in site remediation. In general, soil and groundwater remediation techniques can be divided into two classes depending on whether the pollutant is directly removed/degraded in-place or not, i.e., in situ or ex situ. One of the main problems associated with ex situ remediation is its high operating cost for activities like soil excavation and groundwater pumping. In recent years, in situ techniques have become popular. However, knowledge on processes and factors controlling the results of in situ remediation methods is lacking. This is a problem that can translate to much inflated expenses. Several mathematical models have been proposed to furnish representations close to the real effects of widely known remediation techniques. Some quantitative models have also been proposed for coupling multiphase flow and transport in a porous medium, with consideration of various remediation strategies such as water pumping, vapor and air venting, and steam injection. All of these techniques rely on human intervention for removing the contaminants. These techniques are fast, but costly. Moreover, most of them are too complex and not easily comprehensible for managers and engineers in industries and governments [6]. Therefore, new approaches are needed for developing useful, cost-effective, and user-friendly systems which can be readily adopted by industry and/or government to support decision-making on site remediation actions. Previous studies have provided a variety of remediation methods/technologies, which are available commercially or from research agencies. A comprehensive database of remediation technologies has been developed. For example, Water Technology International Co. developed a Site Remediation Technology Database (REMTEC) which provides a valuable source of information related to site remediation technologies [17]. However, different contaminated sites have varied characteristics depending on pollutants properties, hydrological conditions, and a variety of physical (e.g., mass transfer between different phases), chemical (e.g., oxidation and reduction), and biological processes (e.g., aerobic biodegradation). Thus, the methods selected for different sites vary significantly. The decision for a suitable method at a given site often requires expertise on both remediation technologies and site hydrological conditions [12].

2 324 Z. Chen et al. / Expert system for site remediation In recent years, Artificial Intelligence (AI) technologies have been pervasively applied to many fields. Automation of engineering selection tasks has been studied in a number of process industries. For example, Feng et al. [9] developed a fuzzy expert system for monitoring chemical processes, predicting incidents and providing operation support for process operators. The reasoning strategy of the system involves using a dynamic membership function of a fuzzy system. Lau and Wong [13] presented a methodology for implementation of a fuzzy expert system, using a non-mathematical approach which is able to handle complex closed-loop control situations. The system also demonstrates its advantages over conventional Proportional-Integral-Derivative (PID) closedloop control. Zhang et al. [18] developed an expert system called Coal Mining Expert and Optimization Consultation System (CMEOC). The system determines the underground mining methods, open-pit mining transportation systems, and mining machinery for given conditions and integrates fuzzy sets and optimization methods. Other examples of automated systems developed for selection in process design include the selection of activation systems in oil production [10] and selection of solvent for removal of acidic gases [4,5]. In comparison to these studies that focus on automation of the selection task in the process industries, however, research that focus on applying a knowledge-based approach to automating the selection task in the problem domain of petroleum waste management is scarce. In this paper, a rule-based expert system (ES) for the selection of remediation technologies will be presented. The techniques of knowledge engineering and knowledge acquisition for construction and implementation details on the system will be discussed. Finally, the prototype ES will be applied to real data on a petroleum-contaminated site in western Canada. 2. Development of the expert system 2.1. Overview of the study problem Nonaqueous phase liquids (NAPLs) are hydrocarbons that exist as a separate, immiscible phase in contact with water and/or air. Differences in the physical and chemical properties of water and NAPLs result in the formation of a physical interface between the liquids which prevents the two fluids from mixing. Nonaqueous phase liquids are typically classified as either light nonaqueous phase liquids (LNAPLs) which have densities lower than that of water, or dense nonaqueous phase liquids (DNAPLs) which have densities greater than that of water. Accidental emission of LNAPLs has affected groundwater quality at many sites across North America. The most common LNAPLs-related groundwater contamination problems result from the release of petroleum products. These products are typically multi-component organic mixtures composed of chemicals with varying degrees of water solubility. The LNAPLs inside subsurface represent potential long-term sources for continued groundwater contamination in many areas. The problem domain in this study involves a vast amount of knowledge and decision processes related to site remediation practices. Concisely, the site contaminated by petroleum products is the target for remediation. Information on the site hydrogeology, subsurface contamination, and contaminant transport and conversion are factors relevant for determining the remediation technology. These factors were all considered in the development of the expert system. Knowledge engineering for constructing the decision support system on remediation technique selection involves three stages: knowledge acquisition (KA), conceptual design, and system implementation. In the knowledge acquisition phase, the objects and decision processes were clarified and determined. In the conceptual design stage, the knowledge was formalized and represented with various representation methods, such as decision trees and fuzzy membership functions. Then the formalized knowledge was represented in production rules in the knowledge base of the system. The three phases are presented in detail as follows Knowledge acquisition Knowledge acquisition took the form of interviews conducted over four weekly sessions for 12 experts, with each session lasting two hours. The third author acted as the knowledge engineer, and the first and second authors acted as the domain experts. Both of the two co-author experts had multi-year experience in industrial waste management and site remediation (over 15 years for Huang and 6 years for Chen). The first interview was expert-driven in that the expert prepared the material before the meeting and introduced and explained the concepts and tasks of the problem domain to the knowledge engineer. In the later interviews, when the knowledge engineer had gained some familiarity with the domain, he analyzed the materials obtained in the previous knowledge acquisition sessions and prepared the questions for the subsequent meeting. This process continued until the knowledge engineer was satisfied that the material was sufficient for conceptual modeling. Although development of the ES was mainly based on two co-author experts, their feedback was in fact from multiple sources including not only their own experiences but also a number of research papers, technical reports and web sites, as well as inputs of many other experts in industries and research organizations. In addition to the human expert, a secondary knowledge source was found in the remediation database, which is a commercial database consisting of several hundred remediation methods. The database includes descriptions of the remediation methods and the conditions in which they are suitable. The database provides much useful information but has poor retrieval mechanism, and was used in the project as a source of reference material. The names of the remediation methods as stated in the records formed part of the main decision tree of the system according to the expert s opinion.

3 Z. Chen et al. / Expert system for site remediation 325 The developed ES has been not only tested by the authors in several real-world case studies but also used by many other experts. Feedback from these other experts was collected and analyzed to improve the developed ES. Although some discrepancies might sometimes arise, they were mostly resolved through discussions, communications and comparisons, with agreements being eventually gained. These testing and application processes were helpful for maturing the ES. A number of heuristics were clarified through knowledge acquisition. The domain expert decided that there are a number of factors crucial for the selection of remediation technologies. They are discussed as follows. (1) Contaminated site. There are three types of contaminated sites: soil, groundwater, or both soil and groundwater. Therefore, there are three possible polluted situations: soil is contaminated, groundwater is contaminated, and both soil and groundwater are contaminated (2) Hydraulic conditions of a site. The hydraulic properties of a site include the following considerations: (1) soil permeability determines if it is easy to transport solute or fluid in the soil, (2) whether the site is homogeneous, i.e., soil property is the same at different locations, or heterogeneous, i.e., soil property varies at different locations, and (3) if the site is isotropic, i.e., soil property is the same in different directions, or anisotropy, i.e., soil property varies in different directions. We can classify a contaminated site according to these properties into two classes: simple media and complex media. For example, if the site media have low soil permeability and are homogeneous and isotropic, the site has simple media, but if the media have high soil permeability and are heterogeneous and anisotropy, the site has complex media. (3) Estimated volume of contaminated soil and groundwater. The remediation technique is also determined by the estimated volume and area of the contaminated site. If the area of the site is less than 1600 m 2 and the volume is less than m 3, it is considered a small site, and an ex situ remediation technique can be used. If the contaminated area and volume exceed 1600 m 2 and m 3, respectively, then it is defined as a large site and in situ remediation technique is preferred. (4) Density of the immisible petroleum contaminant. If the density of the immisible petroleum contaminant is lower than water, then relatively simple methods can be applied. If the density of the immisible petroleum contaminant is higher than water, then some advanced remediation methods have to be considered. (5) The immisible contaminants present as free phase or residual phase. If the immisible contaminants are present as free phase, then oil recovery need to be considered. If the immisible contaminants are present as residual phase, then more remediation actions like integrated technology are needed. (6) Concentration range of chemicals in soil and groundwater. The concentration range of chemicals in soil and groundwater were classified into three types: low (0 5 times of standard), high (5 50 times of standard), extremely high (greater than 50 times of the standard). These ranges are used by the system for generating an overall contamination level using a fuzzy membership function and statistical methods. Different contamination levels require different remediation methods. The standard is assumed to be the Saskatchewan standard for the maximum acceptable level of the contaminant Conceptual design Task decomposition In the knowledge engineer s analysis of the elicitation results, we used both a top-down analysis approach and an object oriented technique of knowledge analysis known as Inferential Modeling Technique [2,3]. By adopting the topdown approach, the knowledge engineer began by identifying the main task of the system and then subdividing the main task into several subtasks. This process of decomposition continued until every subtask can be easily implemented. For the system, the main task is to determine the appropriate remediation method for a given contaminated site with specific characteristics. Remediation method selection depends on parameters on the media and contaminant of the polluted site, such as the media type, the contamination level and the size of the media. The decision processes involving these parameters constitute the subtasks. The task structure of the system is shown in figure 1. Both the main task and the subtasks can be represented as decision trees. The main decision tree corresponds to the main task of the system which is how to decide the remediation method for a given situation; this is shown in figure 2. In the main decision tree, the leaf of the tree represent actions to be performed by the system or parameters whose values are to be provided by system users as inputs. Since determination of the remediation method depends on values of media parameters, the system performs inferencing by gathering input data and then making a recommendation. In other words, forward chaining was used as the inference mechanism for the system. Since the determination of remediation methods depends on media parameters as discussed earlier, a number of auxiliary decision trees were formulated which correspond to the subtasks of the system. For example, one subtask is to decide the type of the media, whether it is complex or simple. The decision tree representing this subtask is shown in fig- Figure 1. Structure of the tasks.

4 326 Z. Chen et al. / Expert system for site remediation Figure 2. Decision tree. objects including media, petroleum waste and remediation methods, relationships between the objects of media and remediation methods. Figure 3. Decision tree for subtask of deciding media type. ure 3. Several rules can be derived from this decision tree, an example is as follows: if the composition of the media is homogeneous and the direction of deposit in media is isotropic and the permeability of the media is high then the media is complex media Object-oriented modeling We also adopted an object-oriented approach to knowledge modeling during the conceptual design phase because it is an intuitive way of conceptualizing the domain and also because the implementation tool, G2 (trademark of Gensym Corp., USA), is an object-oriented real-time expert system shell. The inferential modeling technique [2,3] is the objectoriented approach for analyzing domain knowledge adopted in the project. Some knowledge elements specified using the technique include: Since determining the level of contamination that result from the four contaminants is highly subjective and based on heuristics, fuzzy logic is useful for representing this imprecise knowledge. Fuzzy expert systems show exceptional performance for working with processes which are adequately defined in qualitative terms and for which no precise mathematical model of the process exists [19]. This characterization exactly describes the process of determining the pollution level that results from the four most common and important contaminants: benzene, toluene, ethyl-benzene, and xylenes. The four pollutants contribute differently to the final level of contamination, which is defined by the weighted sum of the fuzzy value of each pollutant. The values of the weights are given by the domain expert. The fuzzy functions in the system used for determining the level of contamination is discussed as follows. Firstly, the system calculates the fuzzy value of each pollutant based on the concentration of the pollutants which the user inputs into the system. The fuzzy function for calculating the contamination level of benzene is shown in figure 4. The X-axis denotes the number of times the concentration of benzene exceeds the standard acceptable level of the compound. The maximum acceptable level, or the standard of benzene concentration in soil is 5 µg/g [6,11] which is a fixed value. If the concentration the user inputs is 100 µg/g, the concentration will be twenty times that of the acceptable level (100/5 = 20). So X equals to 20. Y-axis denotes the fuzzy membership grade of the contamination level of benzene, called fb in the system.

5 Z. Chen et al. / Expert system for site remediation 327 where weight_b, weight_t, weight_e and weight_x are weights of the four pollutants contributing to the integrated contamination level; and fb, ft, fe, and fx are membership grades for contamination levels of the pollutants. A number of experts were consulted through questionnaires to obtain priorities of the four typical contaminants; this was followed by statistical analyses of the experts inputs to eventually extract the weights. The obtained weights were then validated through investigation of the four contaminants toxicity under various conditions, based on survey of research papers, technical reports, governmental regulations, as well as interactions with related professionals. Many studies have been done on the toxicity of BTEX. The inhalation of trace levels of toluene, ethyl-benzene and xylenes may cause headache, dizziness and irritation of mucous membranes. In higher concentrations, they can lead to a reduced ability of co-ordination. Long-term exposure of them have been proven to cause brain damage, but none of them is carcinogen. In comparison, benzene is the only proven carcinogen among BTEX. Upon exposure to benzene, the benzene will move into the blood stream. From the blood stream it can get into fatty tissues where it can undergo reactions that produce phenol, which is an even more serious carcinogen than benzene [20]. Thus, according to regulations of USEPA and Environment Canada, benzene is more toxic (carcinogenic) than the other three contaminants, and is more strictly regulated; this well matches the obtained weight distribution of 0.5, 0.2, 0.15 and 0.15 for benzene, toluene, ethyl-benzene and xylenes, respectively. The result of F in the equation determines the general contamination level. The thresholds to categorize the contamination grades were determined based on the experts inputs and the related literature. If F<0.1, then the contamination grade is low ; if F>0.8, the contamination grade is extremely high ; otherwise, it is considered as high. Normally, the leaked petroleum products contain all the four contaminants; when concentration of one contaminant in the leaked products is high, those of the other three will be high as well. This results in a high F value when the contamination grade is extremely high System implementation Figure 4. Main user interface. To determine the membership grade for contamination level F that results from all four pollutants, the following equation is used: F = weight_b*fb + weight_t*ft + weight_e*fe + weight_x*fx, System implementation involves mapping the result of the design process into the knowledge base of the system developed with G2. The G2 (trademark of Gensym Corp., USA) is an object-oriented real time expert system shell and a development environment for creating and deploying intelligent real time applications. It offers components such as class hierarchy, objects, rules, procedures and interface items and can be used for building real time or non-real-time systems for monitoring, scheduling, and diagnostic tasks. System implementation using this tool includes the following steps Define classes of objects According to the conceptual design, there are three classes of objects in this system: (a) media, (b) waste and (c) remediation. Each class consists of attributes or properties which are presented as follows; the letter following each attribute indicates its system status ( U for user input value, I for intermediate result calculated or derived by the system, and E for input value provided by the environmental engineer): (a) Media class Media component (soil, groundwater, or both) (U) Media property I (heterogeneous or homogeneous) (U) Media property II (isotropic or anisotropic) (U) Media permeability (high or low) (U) Media type (complex or simple) (I) Media volume (a numeric value) (U) Media area (a numeric value) (U) Zone type (small or large) (I). (b) Petroleum waste class Oil type (dnapl or lnapl) (U) Present form (residual or free phase) (U) Concentration (low, high, or extremely high) (I) Threshold between low concentration and high concentration (a numeric value) (E) Threshold between high concentration and extremely high concentration (a numeric value) (E) Benzene concentration (a numeric value) (U)

6 328 Z. Chen et al. / Expert system for site remediation Benzene concentration standard (a numeric value) (E) Weight of benzene that contributes to comprehensive contaminated level (a numeric value) (E) Ethyl-benzene concentration (a numeric value) (U) Ethyl-benzene concentration standard (a numeric value) (E) Weight of ethyl-benzene that contributes to comprehensive contaminated level (a numericvalue) (E) Toluene concentration (a numeric value) (U) Toluene concentration standard (a numeric value) (E) Weight of toluene that contributes to comprehensive contaminated level (a numeric value) (E) Xylenes concentration (a numeric value) (U) Xylenes concentration standard (a numeric value) (E) Weight of xylenes that contributes to comprehensive contaminated level (a numeric value) (E). (c) Remediation class Technology name (text) (E) Technology description (text) (E). This list consists of attributes deemed essential by the domain expert for selecting remediation technologies. Conceptually the attributes can be divided into those parameters that pertain to the contaminated site and pollutant and those that are thresholds set by the environmental engineer. This distinction is reflected in the implementation, and the properties in the above list are classified into the three categories of (a) user input properties (U), (b) intermediate results derived from calculation based on the user input values (I)and (c) properties whose values are set by the environmental engineer (E). (a) User input properties: These properties are indicated by U in the list. It is important to obtain a complete list of these properties during knowledge acquisition, because changing these properties changes the basic structure of the system, and hence can lead to much work in system maintenance. (b) Intermediate results: These properties are computed with rules and procedures based on the user input properties. They include both system output values and intermediate calculated results. They are indicated in the list as I. (c) Engineer input properties: These properties refer to the threshold values and coefficients of the fuzzy functions, which embody the experts heuristics and can be adjusted during testing and validating. These values can change depending on the location of the site and subjective judgement of the expert. They are indicated in the list as E. The different kinds of properties requires different handling in both system design and implementation. For example, the engineer input properties are initialized before running the program and can be adjusted when running, the user input properties are connected to the interface parameters and the intermediate results are calculated or derived using rules and procedures Create objects Object is an instantiation of a class. This system consists of one media object, one waste object and twenty-nine remedy objects. That is, at any time during runtime, there is only one contaminated site, one compound of petroleum waste, and twenty nine remedy methods under consideration Define rules The rules of this system are derived from the decision trees, a sample of which is shown in figure 2. Each path in the decision tree determines a rule. Each non-leaf node denotes a relevant property of the media and petroleum waste class objects, which when combined with other nodes on the branch determines the comprehensive condition for appropriate remediation technologies. In the decision tree, there is only one leaf node (i.e., terminal) in each path; however, each leaf node may contain multiple decision options. This implies that, given a set of site conditions, a path within the decision tree will be identified, which terminates at a leaf node; within the leaf node, there are several technologies that could be suitable for the site. The following is a sample rule derived from the highlighted branch of the main decision tree shown in figure 2, and is presented first in English and then in the G2 implementation language. In English: If the component of media is soil and the type of media is complex and the zone of media is small and the type of petroleum waste is LNAPL and the present form of petroleum waste is residual and the waste concentration is high, then the remediation method to use is Enhanced Ozone Oxidation technology or Steam Enhanced Soil Vapor Extraction. In G2 implementation: If the media_component of media1 is soil and the media_type of media1 is complex and the zone_type of media1 is small and the oil_type of waste1 is lnapl and the present_form of waste1 is residual and the concentration of waste1 is high then inform this workspace that The remedy is [enhanced ozone oxidation technology] or [steam-enhanced soil vapor extraction]. The rules derived from the decision trees can be combined according to propositional logic truth table values. Combining rules can make the knowledge base more compact and efficient. However if the decision trees are modified and increase in size later, they are more difficult to maintain. This is a tradeoff to be considered in combining rules. Rules are combined under two conditions: (1) the new rule does not violate the semantics of either of the original rules from which it is derived, and (2) the combination does not involve many logical manipulations.

7 Z. Chen et al. / Expert system for site remediation 329 Figure 5. Engineer interface. An additional consideration in constructing the knowledge base was to group the rules from each decision tree into a separate workspace. Since each decision tree illustrates the problem solving process for accomplishing one task objective, the rules are grouped according to objectives such as determining the type of media or determining the final selection of remediation technique. This facilitates invoking rule groups according to workspaces and enables easier maintenance Define interface In order to support the two different types of input and increase flexibility of the system, we constructed two separate system interfaces: one for the user and one for the engineer. The user interface shown in figure 4 enables the user to provide values describing the contaminated site and contaminants. Through this interface, the user provides information to the system about the site such as the media component (whether it is soil, groundwater, or both), properties about the media such as whether it is homogenous or heterogenous, isotropic or anisotropic, whether permeability of the media is low or high, and the area and volume of the media. The user also answers queries about the contamination such as the type of petroleum waste, whether the waste is in free phase or residual phase, and the amount of the four contaminants of benzene, toluene, ethyl-benzene, and xylenes in the soil and groundwater. The engineer interface shown in figure 5 enables the environmental engineer to provide three kinds of information to the system. First, the two threshold values of low and high discretise the membership grade of the contamination level to low, high, and extremely high. The second type of input provides information on the maximum acceptable levels of contamination. The third type of input measures relative contribution of each contaminant to the final contamination level. These are labeled B-weight for the weight of benzene, T-weight for the weight of toluene, E-weight for the weight of ethyl-benzene, and X-weight for the weight of xylenes. All three types of input can be altered during runtime. Any change in the input to this engineer interface can result in recommendation of a different remediation method by the system Define procedures to connect user interface with G2 objects There are three procedures in the remediation advisor. The main procedure is used to transfer values from the interface items to the objects defined in the system, thereby triggering the forward chaining process. The user interface accepts input values which trigger the inference mechanism of forward chaining. However, the G2 environment requires that interface items connect to parameters and variables but not to system defined objects. Hence an explicit link has to be set up between the parameters or variables and the ob-

8 330 Z. Chen et al. / Expert system for site remediation jects. For example, the following command assigns the values from interface item media_component to the property media_component of object media1: Conclude that the media_component of media1 = media_component. Procedures were also used to implement the fuzzy functions. The following is the procedure to determine the contamination level of benzene. It is triggered when the user inputs a value for benzene concentration in the soil denoted in the system as b_soil of petroleum waste. The procedure then generates the fuzzy value of benzene contamination denoted as B_soil_fuzzy. Decide_b_concentration() B_times: quantity; B_soil_fuzzy: quantity; Begin B_times = (the b_soil of waste1)/b_standard; B_soil_fuzzy = 1/50* b_times; If B_soil_fuzzy < 0then B_soil_fuzzy = 0; If B_soil_fuzzy > 1then B_soil_fuzzy = 1; End System validation Two cases were examined using the developed ES, with the derived outputs being compared with those from the source literature [17,21]). The details are provided as follows. (1) Case 1 Input data: Media: soil, Hydrological condition 1: homogenous, Hydrological condition 2: isotropic, Permeability: low, Area: 1,000 m 2, Volume: 8,000 m 3, Waste oil type: LNAPL, State: free phase. System output: Alternative 1: Static-pile bioremediation, Alternative 2: Integrated soil vapor extraction and air sparging. Discussion: (A) Validation of Alternative 1 The system recommends the static-pile bioremediation (SPB) technology for this contaminated site. This recommendation is valid because (a) the free phase of oil products exists in the soil and needs to be removed, (b) the contaminated area and volume are relatively small, which implies that an ex situ technology can be used, and (c) the free-phase oil is semi-volatile in the soil. The SPB technique is designed for decomposing the oil through microbial activities. It is a widely used approach for dealing with petroleum-contaminated soil. During the treatment process, the contaminated soil is placed in piles within which a perforated pipe is installed to supply oxygen to soil microbes. Moisture and nutrients are added through drip irrigation. The soil piles are constructed on a pad or liner to prevent groundwater contamination; they may also be covered to reduce volatilization. Compared with landfarming which is another widely used method for treating petroleumcontaminated soils, this approach requires less space, and is more cost-effective. (B) Validation of Alternative 2 Soil vapor extraction (SVE) has been widely used to remove volatile and some semi-volatile organic compounds from subsurface. This method generally uses vacuum blowers and extraction wells to extract organic contaminants in unsaturated zone. However, SVE is less effective in tackling oil around groundwater table and in saturated zone. Its integration with air sparging will overcome this drawback and thus remarkably increase the removal efficiency. Air sparging means pumping air into the saturated zone to help flush (bubble) the contaminants up into the unsaturated zone where the SVE extraction wells can remove them. Many practical applications of the integrated soil vapor extraction and air sparging technique have been reported [22]. (2) Case 2 Input data: Media: clayey soils, Hydrological condition 1: heterogeneous, Hydrological condition 2: anisotropic, Permeability: high, Area: 500 m 2, Volume: 25,000 m 3, Waste oil type: DNAPL, State: residual, Benzene concentration: 8.9 mg/l, Toluene concentration: 10.1 mg/l, Ethyl-benzene concentration: 4.9 mg/l, Xylenes concentration: 12.4 mg/l. System output: Alternative 1: Thermally enhanced vapor extraction, Alternative 2: Combined physiochemical extraction, Alternative 3: In situ steam flushing/stripping and bioventing. Discussion: (A) Validation of Alternative 1 Thisalternativeisanintegrated approach that consists of shallow soil mixing, thermally enhanced vapor extraction, and soil vapor extraction.

9 Z. Chen et al. / Expert system for site remediation 331 It is an in situ enhanced vapor extraction technique. The site in this case is contaminated by not only waste heavy oil (DNAPLs) but also constituents like benzene, toluene, ethyl-benzene, and xylenes, all of which have concentrations higher than acceptable levels. In addition, the soil conditions (clayey soils) are more disadvantageous than those in case 1, with a large contaminated soil volume. Hence, conventional ex situ techniques are not applicable. The proposed in situ enhanced soil vapor extraction technology can remove more than 70% of pollutants in the soil. The clayey soils are handled by injecting hot air through a mixing auger that penetrates through the soils without the need of excavation. This technology is especially suitable for sites with a large volume and a high depth of contaminated soil. Volatile organic compounds are removed by vapor extraction through wells as well as a special movable shroud that covers the work area during the mixing process. (B) Validation of Alternative 2 In combined physiochemical extraction, a solvent is used to separate or remove organic contaminants from soil; this is combined with soil washing based on the consideration of the disadvantageous on-site soil conditions. The solvent extraction is effective in treating sediments, sludges, and soils containing organic contaminants such as VOCs and petroleum wastes. This combined process has been shown to be applicable for many practical cases [23]. Its main limitation is that trace of the solvent may remain within the treated soil. Thus, toxicity of the solvent is an important factor to be considered. This technology is suitable for sites with large contaminated soil volumes like this presented case. (C) Validation of Alternative 3 This alternative consists of two phases. In phase one, steam is introduced to the aquifer through injection wells to vaporize volatile and semivolatile contaminants. Vaporized components rise to the unsaturated (vadose) zone where they are removed by vacuum extraction and then treated. In phase two, bioventing is a used to stimulate in situ biodegradation of aerobically degradable contaminants in soil by providing oxygen to existing soil microorganisms. In contrast to soil vapor extraction, bioventing uses low air flow rates to provide only enough oxygen to sustain microbial activities. Oxygen is supplied through direct air injection into residual contamination zones in soil. In addition to degradation of adsorbed fuel residuals, volatile compounds are biodegraded as vapors move slowly through biologically active soil. This technology has been demonstrated to be very effective in dealing with many practical cases [24]. In the above two case studies, the obtained system outputs are similar to decisions made for equivalent cases by other researchers. In both cases, the resulting recommendations are appropriate, demonstrating applicability of the developed system for decision support. 3. Application to a petroleum-contaminated site 3.1. The study system The study site is located in western Canada. It was operated as a natural gas processing plant from mid 1960 s to early 1990 s. The plant was utilized to remove naphtha condensate from the natural gas stream prior to transport to a regional transmission line. Throughout the history of the site, the condensate was disposed of in two perforated underground storage tanks (USTs) (figure 6), which then leaked into the soil and finally to groundwater following seepage. Two contaminant concentrated zones were formed in the subsurface capillary zone (at the interface between unsaturated and saturated zones). The site is bounded in all directions by agricultural land. There are several farmer residences located within a 2 km radius of the site all with domestic water withdrawal wells. Groundwater was encountered between 5 and 10 m below surface. The general groundwater flow direction is towards the south, with the gradient of water table being slightly from northeast to southwest. The groundwater table is predominately located within a clay-till soil layer. The volume of contaminated soil and groundwater (in saturated and unsaturated zones) was estimated to be about 8,000 8m 3. Pollutants including benzene, toluene, ethylbenzene, and xylenes have been found in contaminated subsurface. In addition, site investigations indicate that the site primarily consists of clay and tilt, whose permeability is very low. Before any remediation technology is considered through the proposed ES, the information at the study site was investigated intensively. They are documented as follows Site condition (1) Surface conditions. Elevation of the site is approximately 600 m above sea level. The geomorphology at the site consists of a hummocky glaciolacustrine/moraine environment. Materials within the moraine environment consist of glacial drift which may contain till, clay, silt, sand or gravel. Materials within the glaciolacustrine environment consist of stratified drift which may contain clay, silt or sand. Figure 6. The study site.

10 332 Z. Chen et al. / Expert system for site remediation (2) Hydrology. Hydrological conditions at the site were analyzed based on aerial photographs and topographic maps. Surface drainage from the site flows further south and then east towards an alkaline slough. (3) Geology. Geology of the site consists of an approximately 50 m deep undifferentiated glacial drift over a bedrock layer of the Judith river formation which is primarily comprised of gray, noncalcareous silt, and clay. The glacial drift is composed mainly of clay and clay till interbeded with sand layers. (4) Stratigraphy. The stratigraphy at the depth of 0.05 to 0.15 m consists of a thin sand and gravel fill. Clay till underlies the fill and extends to depths varying from 1.1 to 12.2 m. Sand underlies the clay till in most areas of the site except the immediate south. Clay till underlies the sand over most of the site. The sand was discountinous and ranged in thickness between 0.02 and 5.5 m. The sand was generally encountered at the depths of 5.0 to 12.0 m. (5) Groundwater conditions. Groundwater was encountered between 4.5 and 10 m below surface. In the UST 1 area, the groundwater table was encountered at 5.7 to 9.1 m below surface, while that at the north of the UST 1 was at 4.7 to 9.2 m deep. The general groundwater flow direction estimated from all previous site investigation reports is towards the south. The groundwater elevations were measured through the monitoring wells from 1993 to 1997, and the general groundwater flow direction is towards southeast, with the gradient of water table being slightly from northwest to southeast. The only exception is at the site s immediate south, where higher gradients can be found at some spots. The groundwater table is predominately located within the clay-till soils Site contamination Investigation results of groundwater quality at the site from 1993 to 1997 are obtained from previous monitoring records. They are summarized as follows. (1) Free liquid phase contaminants. In February and March 1996, free phase hydrocarbons were encountered in 5 wells. The largest fuel thickness was 183 mm. The volume of free product was estimated to be between 0.20 and 1.00 m 3. (2) Residual phase in soil. In February 1996, combustible soil vapor concentrations exceeding 10,000 ppm were detected in soil samples obtained at depths of 5.0 and 8.5 m in two wells. The location of these elevated vapors was primarily in clay till. However, the sandy silt and the thin sand seams also contain the elevated, combustible vapor with high concentrations. (3) Dissolved phase in groundwater High concentrations of Total Petroleum Hydrocarbon (TPH) and benzene, toluene, ethyl-benzene, and xylenes (BTEX) are found in a number of wells. The highest TPH concentration and benzene were 175 mg/l and 7.58 mg/l, respectively, which were beside the UST 1. (Note: the regulated TPF and benzene concentration in the guidelines of drinking water from USEPA are 5 and 1,000 µg/l, respectively.) Another highly contaminated area was beside the facility with peak TPH concentration of 32 mg/l and peak benzene concentration of 7.22 mg/l Result analysis When conditions of the site and contaminants are considered, the expert environmental engineers (i.e., the first and second authors) recommended four remediation technologies: In situ remediation technologies soil vapor extraction with air sparging, in situ bioremediation. Ex situ remediation technologies excavation/landfarming, excavation/low temperature thermal desorption. When the same conditions of the site and contaminants are input to the prototype expert system, it recommends the remediation technology of soil vapor extraction with air sparging. That is, only the first technology in the above list is suggested by the system. The present version of the system is primarily rule-based and it only makes a single recommendation. Future extensions of the system would incorporate more complex fuzzy modeling of the conditions and the system would be able to output more than one recommendation. The single recommendation has been validated by the expert environmental engineers. Hence, the validation exercise constitutes partial validation of the prototype system which cannot handle comprehensive considerations and tradeoff analyses of many uncertain factors, such as contaminant volatility, soil permeability, cost, efficiency, and cleanup time. The following are detailed descriptions of the remediation alternatives. (1) Landfarming Landfarming is one kind of ex situ bioremediation technologies by which contaminated soils are excavated from the site and spread over a designated area to remediate the contaminants. This technology involves spreading the material in a thin layer and adding nutrients and water to provide conditions suitable for accelerated degradation. The soil is frequently aerated by discing or ripping to expose new areas to the additives and oxygen from the air. Tilling the soil also encourages volatilization of the lighter portions, while the remaining compounds are immobilized with the soil mass. The hydrocarbon contamination can then be physically and biologically degraded through these enhanced processes. For the study site, there are approximately 10,000 m 2 of on-site land, which can be used for spreading and mixing the contaminated soils. There is also plenty of groundwater in

11 Z. Chen et al. / Expert system for site remediation 333 subsurface for landfarming needs. Since the site is far away from populated towns, impacts of hydrocarbon volatilization from the landfarming area are insignificant. Although long cold winters prevail over the site, there are still 6 months suitable for landfarming each year. These facts demonstrate that landfarming is suitable for the study site. (2) Low temperature thermal desorption Low Temperature Thermal Desorption (LTTD) involves the volatilization of hydrocarbon compounds without heating the soil matrix to combustion temperatures. It is a process that uses either indirect or direct heat exchange to heat organic contaminants to a temperature high enough to volatilize and separate them from contaminated soils. Air is usually used as the transfer medium for the vaporized components. For the study site, these potential limitations could be potentially overcome. For example, the water content of soil at the site is between 5% to 26%, not very high for thermal treatment. Since the site is far from populated towns, the impacts of fugitive dusts generated from clay soils would be insignificant. In this project, the low temperature thermal desorption technology is considered to be an alternative to landfarming for treating excavated soils from the site, due mainly to its relatively short treatment time. (3) Soil vapor extraction and air sparging Soil Vapor Extraction (SVE) is a commonly used technology for removing VOCs in vadose zone soils, which has been recommended by the USEPA as a presumptive remedy. The SVE is an in situ technology that uses vacuum blowers and extraction wells to strip volatile compounds from unsaturated soil. Vacuum blowers supply the motive force, inducing air flow through the soil matrix. The air strips the volatile compounds from the soil and carries them to the screened extraction wells. The extracted vapors are then treated at the surface and released to the atmosphere or reinjected into the subsurface. Since soils at the study site have low permeability, some enhancement measures like Pneumatic Fracturing Enhancement (PFE) need to be undertaken in combination with the SVE. One of the limitations of SVE alone is that it does not effectively deal with contaminated soils within the capillary fringe and below the groundwater table. Air sparging can enhance the remediation capabilities of SVE in the capillary fringe zone such that chemicals with lower volatility and/or chemicals that are tightly absorbed can be removed. Air sparging can also enhance biodegradation of aerobically degradable contaminants so that the remediation time can be shortened. (4) In situ bioremediation In situ biodegradation has been shown to be effective at degrading or transforming a large number of organic compounds to environmentally acceptable or less mobile compounds. Soluble organic contaminants are particularly amenable to biodegradation. Relatively insoluble contaminants may also be degraded if they are accessible to microbial degraders. In general, petroleum hydrocarbonsobserved at the study site are very amenable to biodegradation. For the study site, in situ bioremediation can be used to restore groundwater, and can be combined with enhanced SVE for onsite soil remediation. (5) Integrated approaches Given the complexities associated with the hydraulic and contamination conditions at the site, integrated approaches combining several technologies within a general remediation system are in particular desirable. Several integrated approaches are obtained based on further in-depth examination of the site characteristics and the related technologies. As a result, six remediation alternatives were recommended as follows: excavation/landfarming + in situ bioremediation excavation/landfarming+ soil vapor extraction + in situ bioremediation excavation/landfarming + soil vapor extraction + air sparging + in situ bioremediation excavation/low temperature thermal desorption + in situ bioremediation no-excavation/in situ bioremediation no-excavation/soil vapor extraction + air sparging + pneumatic fracturing enhancement + in situ bioremediation. 4. Discussions (1) Considerable knowledge and judgement is required to assess the situation of site contamination sufficiently, to formalise the input variables, and to interpret which of a range of remediations is most appropriate. Due to this complexity, it has been hard for environmental engineers to directly judge applicable technology(ies). Many engineers desire a system to help them (a) identify potential technologies, and (b) obtain technical details of each suggested technology. The developed ES can incorporate a variety of complicated knowledge and interactive relationships within a general framework. The decision alternatives it recommends are based on comprehensive and systematic analyses of inputs from experts and existing literature; they are generally reliable, if not optimal, since each recommended alternative is based on several experts previous experience(s). This role (from the developed ES) is highly appreciated by engineers and managers in this professional field, since it allows them to at least conduct preliminary screening of the existing technologies and identify those with lower risks. (2) In addition to indexing the range of remediation strategies, the developed ES also provides many details of the recommended strategies (e.g., efficiency, cost and time requirement) as well as the experts comments and suggestions based on their personal experiences. The ES provides

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