Scientific Innovation of Bioremediation Technologies

Transcription

1 Journal of Scientific & Industrial Research Vol. 74, April 2015, pp Scientific Innovation of Bioremediation Technologies Grace T R Lin* and Yen-Chun Lee Institute of Management of Technology, National Chiao Tung University, 1001, Ta-Hsueh Road, Hsinchu City 30010, Taiwan Received 6 August 2014; revised 29 January 2015; accepted 2 March 2015 This study aimed to identify the major research and development (R&D) trends of bioremediation technologies, as well as to build the core technology category framework through the collection of the United States Patent and Trademark Office s (USPTO) patent information. The Patent Co-citation Analysis (PCA) method and the factor analysis technique were applied to categorize the patents of bioremediation and to identify the main technology trends behind it. The result revealed that the major R&D trends comprise the fields of anaerobic degradation, aerobic degradation, anaerobic reductive dechlorination, bioaugmentation, and bioventing. Next, we further applied the fuzzy Delphi method, following former study results, which were chiefly derived from the aforementioned PCA, in order to investigate and decide upon the most studied country of Taiwan, which had had emergent needs for the benefits of developing novel bioremediation technologies in particular. Keywords: Bioremediation, Technology Category, USPTO, Patent Co-citation Analysis, Fuzzy Delphi Method. Introduction Pollutants enter the environment directly as a result of accidents, spills during transportation, leakages from waste disposal or storage sites, or from industrial facilities 1. The cost to recover the contaminated environment is extremely high. For an example, remediation costs for sites contaminated with hazardous wastes in Europe are expected to exceed 1.5 trillion U.S. dollars in the near future 2. Hence, there is an urgent need for cost-effective treatment approaches to deal with these various contamination problems. In recent years, bioremediation has become a rapidly developing field of environmental restoration, utilizing natural microbial activity to reduce the concentration and/or toxicity of various chemical substances 3.Past studies have proved that patent data can provide researchers with an overall picture of a specific technology s developing trend and streams 4,5. In this regard, this study aimed to apply the Patent Co-citation Analysis (PCA) method to classify the patents of bioremediation searched within the database of United States Patent and Trademark Office (USPTO) to identify the main technology trends. Moreover, this study further applied the fuzzy Delphi method, following former study results, chiefly derived from the aforementioned * Author for correspondence gtrl@faculty.nctu.edu.tw PCA, in order to investigate and decide upon the most studied country of Taiwan, which had had emergent needs for the benefits of developing novel bioremediation technologies in particular. Methodology This study utilized two major methods to explore bioremediation technology trends globally and nationally. First, in order to generate valuable information from patent data, it was necessary to group patents for further analysis 6. The co-citation analysis proposed by Small 7 was originally used to measure the relationship between two publications in the scientific literature. Using co-citation analysis, the similarity among patents can be determined based upon common patterns of citation. The focus of the co-citation analysis was on computing the frequency of Documents A and B being co-cited by specific documents to demonstrate their similarity. Lai & Wu 8 proposed the PCA to create a patent classification system based upon the concept of co-citation analysis. After the patent classification system was built, target patents were classified by comparing them with basic patents. The concept of PCA was shown in Fig. 1. Based upon the similarities of basic patents, technology categories F 1 and F 2 were identified. Consequently, this study adopted the PCA in order to identify the key research fields of a specific

2 198 J SCI IND RES VOL 74 APRIL 2015 were three steps required to obtain the similarities in the basic patent pairs. Fig. 1 The concept of PCA technology from patent data. The PCA was divided into three phases in order to complete a classification system described as follows. Phase I: Searching for Patents and Defining Industry Basic Patents In phase I, a proper patent database was selected in order to conduct the patent search. As a result, industry basic patents were defined from the search results. Patent Search The selected patents from this step were divided into two groups: target patents and candidate basic patents, where Q p is denoted as the target patent p, and CP q is denoted as a candidate for a basic patent q. Target patents were citing patents that would be classified. Candidates for basic patents were the patents cited by target patents. The referential relationship between target patents and candidates for basic patents was represented as the matrix [α pq ] M N, where M is the number of target patents, and N is the number of candidates for basic patents. The Selection of Basic Patents The more often a specific early patent is cited by later patents, the more likely it is to be the foundation of these later patents 9,10 ; thus, a so-called basic patent is a patent repeatedly cited by later patents 8. The frequency of CP q being cited (CS q ) was shown as CS q = M p=1 α pq. CP q become a basic patent if CS q was greater than or equal to the threshold c for selecting basic patents. After identifying all of the basic patents, a matrix [ε pq ] m n was created from the relationship between the basic patents and the target patents, where P q is a basic patent q, m is the number of target patents that can be classified by the basic patents, and n is the number of basic patents. Phase II: Assessment of the Similarities within Basic Patent Pairs The PCA adopted Pearson correlation coefficient to assess the similarity of a basic patent pair 11. There Calculating the Co-cited Frequency of Each Basic Patent Pair The co-cited frequency of the given basic patents q and q is shown in Equation 1: ω qq = { M p=1 ε pq ε pq if q q 1 q n, 1 q n 0 if q = q (1) A symmetric matrix ω qq can be obtained after computing all of the co-cited frequencies of n basic patents. Calculate the Linkage Strength of Each Basic Patent Pair The linkage strength of a basic patent pair was calculated from Equation 2, which normalized the co-citation frequency by taking into the account the total number of citations for both basic patent q and q 10,12. π qq = { ω qq / (S q +S q +ω qq ) if q q 1 q n, 1 q n 0 if q = q (2) where ω qq is the co-cited frequency calculated in the previous steps and S q = M p=1 ε pq is the cited frequency of a basic patent q. Calculating the Pearson Correlation Coefficient of Each Basic Patent Pair Before calculating the Pearson correlation coefficient of the given basic patents q and q, the linkage strengths of these basic patent pairs were divided into two groups. The first group was Π q = { π rq, r q, q }, and the second group was Π q = { π rq, r q, q }. Next, we calculated the Pearson correlation coefficient of each basic patent pair based on the formula proposed by Lai & Wu 8 to obtain the Pearson correlation coefficient matrix [r pq ], where π rq Πq is the linkage strength between basic patents r and q, and πrq Πq is the linkage strength between basic patents r and q. Phase III: Creation of a Patent Classification System Bibliometrics generally employs factor analysis, cluster analysis, or multi-dimensional scaling to classify documents, journals, and authors. The PCA employed factor analysis to classify patents based on two considerations. First, the loading of patents onto a technology category indicated the degree of importance of the basic patents to the technology. Second, factor analysis can be repeated to create a hierarchical classification system, if necessary 8. The

3 LIN & LEE.: SCIENTIFIC INNOVATION OF BIOEMEDIATION 199 inputs for the factor analysis were the Pearson correlation coefficients, calculated by the third step in Phase II. Fuzzy Delphi Method The fuzzy Delphi method is a useful technique for aggregating experts opinions systematically and reducing the uncertainty and ambiguity existing in the experts' judgments 13,14. This study deployed the fuzzy Delphi method, following the analysis results of PCA, to investigate and draw conclusions about the most country of Taiwan and its development of novel bioremediation technologies. In this study, the double triangular membership functions and the fuzzy theory were applied to solve the group decision. The triangular fuzzy number ~ i O = (O i L, O i M, O i U) for all of the experts most optimistic cognitions for each individual element, and the triangular fuzzy number ~ i C = (C i L, C i M, C i U) for all of the experts most conservative cognition for each individual element were established. Empirical Results Based on the results of expert interviews, this study employed bioremediation as a keyword to search through the database of the United States Patent and Trademark Office (USPTO) 8. This study used the cited frequency greater than 4 as the criterion to identify a basic patent, and 152 basic patents were thus selected from the 1,457 candidates. We found that 199 target patents refer to the 152 basic patents. Thus, the referential relationship between them can be denoted by the matrix [ε pq ] Next, the co-cited frequency of basic patent pairs by 199 target patents was calculated with Equation 1 as denoted by the matrix [ω pq ] The matrix was then computed with Equation 2 for obtaining the linkage strength as denoted by the matrix [π pq ] Finally, the correlation coefficient matrix [r pq ] was factor-analyzed through principal component analysis. 13 factors were retained due to their eigenvalue greater than 1. Moreover, from the scree test shown in Fig 2, the first 5 factors were retained with more than 79% of the cumulative variance. Table 1 shows the technology categories named by experts, and the patent within the five technology categories.aerobic bioremediation is thermodynamically most favorable for the cleanup of reduced pollutants such as hydrocarbons. On the other hand, highly chlorinated Fig. 2 The scree test of factors eigenvalues Table 1 The technology categories of bioremediation Category Aerobic Reductive Dechlorinatio n Bioaugmentati on Bioventing UPC Code Title Apparatus and method for confining and decontaminating soil Method and apparatus for subsurface bioremediation Bioremediation systems and methods (The other 22 items are not listed here due Bioremediation of contaminated groundwater Underground contamination in situ treatment system Pneumatic fracturing and multicomponent injection enhancement of in situ bioremediation (The other 12 items are not listed here due In-situ bioremediation of contaminated groundwater Enhancement of in situ microbial remediation of aquifers Electron donors for chlorinated solvent source area bioremediation (The other 6 items are not listed here due Pulsing of electron donor and electron acceptor for enhanced biotransformation of chemicals Fixed bed bioreactor remediation system Microbial degradation of DDT (The other 6 items are not listed here due System and method for decontamination of contaminated ground Mono-well for soil sparging and soil vapor extraction Contaminant remediation, biodegradation and volatilization methods and apparatuses (The other 5 items are not listed here due

4 200 J SCI IND RES VOL 74 APRIL 2015 Table 2 Fuzzy Delphi screening result of bioremediation evaluation Category Geometric Mean Consensus Gray Zone G i Result C i O i Single Value no overlap selected Aerobic (6, 8) ignored Reductive (7, 9) selected Dechlorination Bioaugmentation (5, 7) ignored Bioventing (4, 6) ignored compounds have already been oxidized and may degrade faster anaerobically by reductive dechlorination 15. In the process of anaerobic reductive dechlorination, the compound serves as an electron acceptor. All chlorinated aliphatics are susceptible to anaerobic, cometabolic, reductive dechlorination. Bioaugmentation refers to the addition of exogenous, specialized microorganisms with enhanced capabilities in order to degrade the target pollutant. Bioventing is an in-situ remediation technology that uses indigenous micro-organisms to biodegrade organic constituents adsorbed to soils in the unsaturated zone.next, this study further applied the fuzzy Delphi method, following former analysis results, to investigate and decide upon the most studied country of Taiwan. Table 2 revealed that both anaerobic degradation and anaerobic reductive dechlorination are the two better candidates for Taiwan. Conclusions Bioremediation is defined as use of biological processes to degrade, break down, transform, and/or essentially remove contaminants or impairments from soil and water. The main contributions of this study are: (1) providing a useful way to identify the core framework and developing streams of a specific technology through patent information analysis; (2) demonstrating how to apply the PCA method step by step; (3) conducting an empirical analysis of bioremediation technologies that are mostly concerned in our society today; and (4) investigating the optimal bioremediation alternatives for Taiwan. This study effectively employed the PCA method together with the factor analysis technique to classify the patents of bioremediation and identify its technology category. The results showed that the major research and development (R&D) trends comprise the fields of anaerobic degradation, aerobic degradation, anaerobic reductive dechlorination, bioaugmentation, and bioventing. The research analysis of PCA method could serve as a reference to future R&D development in the field of bioremediation technology.besides, this study further applied the fuzzy Delphi method following the former analysis results to investigate and decide upon the most appropriate bioremediation technologies for the case studied country of Taiwan, with its emergent needs to prospect for novel bioremediation technologies. The analysis results revealed that both anaerobic degradation and anaerobic reductive dechlorination are the two better bioremediation technology candidates for Taiwan, mainly due to the fact that the country had faced its most serious threats from industrialization and urbanization. It was subsequently suggested for future researches to consider how different environmental, economic, technological and social scenarios should lead to corresponding bioremediation technology exploitation. References 1 Liu R, Jadeja R N, Zhou Q & Liu Z, Treatment and Remediation of Petroleum-Contaminated Soils Using Selective Ornamental Plants, Environ Eng Sci, 29 (2012) Wang L K, AUTORES V, Wang M H S, Hung Y T, Shammas N K & Chen J P, Handbook of Advanced Industrial and Hazardous Wastes Management (CRC Press, Boca Raton) 2013, Kumar A, Bisht B, Joshi V & Dhewa T, Review on Bioremediation of Polluted Environment: A Management Tool, Int J Environ Sci, 1 (2011). 4 Cesaroni F & Baglieri D, Technology intelligence: New challenges from patent information in Information Systems: Crossroads for Organization, Management, Accounting and Engineering (Springer, Boston) 2012, Chang P L, Wu C C & Leu H J, Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display, Scientometrics, 82 (2010) Breitzman A F & Mogee M E, The many applications of patent analysis, J Inf Sci, 28 (2002) Small H, Co-citation in the scientific literature: A new measure of the relationship between two documents, J Am Soc Inform Sci, 24 (1973) Lai K K & Wu S J, Using the patent co-citation approach to establish a new patent classification system, Inform Process Manag, 41 (2005) Mogee M E, Patent analysis methods in support of licensing, Proc of the Technol Transfer Society Annual Meeting (International Symposium and Exhibit, Denver Colorado) 1997,

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