1,864 research outputs found

    Optimizing an in Situ Bioremediation Technology to Manage Perchlorate-Contaminated Groundwater

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    Combining horizontal flow treatment wells (HFTWs) with in situ biodegradation is an innovative approach with the potential to remediate perchlorate-contaminated groundwater. A technology model was recently developed that combines the groundwater flow induced by HFTWs with in situ biodegration processes that result from using the HFTWs to mix electron donor into perchlorate-contaminated groundwater. A field demonstration of this approach is planned to begin this year. In order to apply the technology in the field, project managers need to understand how contaminated site conditions and technology design parameters impact technology performance. One way to gain this understanding is to use the technology model to select engineering design parameters that optimize performance under given site conditions. In particular, a project manager desires to design a system that: 1) maximizes perchlorate destruction; 2) minimizes treatment expense; and 3) attains regulatory limits on down gradient contaminant concentrations. Unfortunately, for a relatively complex technology with a number of engineering design parameters to determine, as well as multiple objectives, system optimization is not straight forward. In this study, a multi-objective genetic algorithm (MOGA) is used to determine design parameter values (flow rate, well spacing, concentration of injection electron donor, and injection schedule) that optimize the first two objectives noted; to maximize perchlorate destruction while minimizing cost. Four optimization runs are performed, using two different remediation time spans (300 and 600 days) for two different sets of site conditions. Results from all four optimization runs indicate that the relationship between perchlorate mass removal and operating cost is positively correlated and nonlinear

    Optimization and enhancement of soil bioremediation by composting using the experimental design technique

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    The objective of this study was the application of the experimental design technique to optimize the conditions for the bioremediation of contaminated soil by means of composting. A low-cost material such as compost from the Organic Fraction of Municipal Solid Waste as amendment and pyrene as model pollutant were used. The effect of three factors was considered: pollutant concentration (0.1-2 g/kg), soil:compost mixing ratio (1:0.5-1:2 w/w) and compost stability measured as respiration index (0.78, 2.69 and 4.52 mg O2 g⁻¹ Organic Matter h⁻¹). Stable compost permitted to achieve an almost complete degradation of pyrene in a short time (10 days). Results indicated that compost stability is a key parameter to optimize PAHs biodegradation. A factor analysis indicated that the optimal conditions for bioremediation after 10, 20 and 30 days of process were (1.4, 0.78, 1:1.4), (1.4, 2.18. 1:1.3) and (1.3, 2.18, 1:1.3) for concentration (g/kg), compost stability (mg O₂ g−1 Organic Matter h−1) and soil:compost mixing ratio, respectively

    Optimization of Palladium-Catalyzed in Situ Destruction of Trichloroethylene-Contaminated Groundwater Using a Genetic Algorithm

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    Conventional technologies for the treatment of groundwater contaminated with chlorinated solvents have limitations that have motivated development of innovative technologies. One such technology currently under development involves using palladium-on-alumina (Pd/Al) as a catalyst to promote dechlorination. Pd/Al catalyst may be used in-well as part of a re-circulating horizontal flow treatment well (HFTW) system. An HFTW system involves two or more dual-screened wells, with in-well reactors, to capture and treat contaminated groundwater without the need to pump the water to the surface. In this study, objective and fitness functions, based on system costs and TCE concentration requirements, were developed to optimize a dual-well HFTW system with in-well Pd/Al reactors in a two-aquifer remediation scenario. A genetic algorithm (GA) was coupled with a three dimensional numerical model of contaminant fate and transport to determine optimized HFTW control parameters (well location, pumping rate, and reactor size). The GA obtained a solution within the specified constraints, but the solution was an artificial solution, as contaminated groundwater in one of the two aquifers received no treatment. Based on these results, new objective and fitness functions were developed in an effort to determine the most cost effective solution to remove contaminant mass from the aquifer. The solution arrived at using this approach, while resulting in minimized values of cost per contaminant mass destroyed, produced unacceptably high downgradient contaminant concentration levels. We conclude that by specifying that only two wells could be used in the HFTW system, we overconstrained the problem and that a multi-well HFTW solution is required

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Evaluating Sustainable Aspects of Hazardous Waste Remediation

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    The main objective of the research presented herein is to be a major contributor to the current international initiative to advance sustainability assessments for remediation projects by integrating methodologies from the environmental economics and social science disciplines. More specifically, the study aims to address some of the knowledge gaps related to conducting a comprehensive sustainability assessment for a remediation project. These knowledge gaps include: (1) there are few studies that include sustainability assessments of the variety of techniques and technologies implemented during site characterization; (2) the majority of sustainable remediation publications and assessment tools focus on evaluating the environmental impact of a contaminated site’s life cycle and minimally, if at all, on related socio-economic impacts; and (3) the role of risk perception in stakeholder engagement has not been explored in existing sustainable remediation frameworks. Chapters 2 through 4 presents a societal cost analysis methodology to quantify global socio-economic impacts arising from cleanup activity by monetizing the emissions and energy consumption through the integration of the social cost of environmental metrics. The results of environmental footprint and life cycle assessment evaluations conducted at various stages throughout the project life cycle were used as the basis for the societal cost analysis. Chapter 5 presents a survey developed and implemented to identify risk perception factors that influenced residents’ level of participation in risk management activities conducted by the local health department. Based on the case study evaluations presented herein, it can be concluded that the integration of methodologies from the environmental economics and social science disciplines into existing sustainable remediation frameworks results in a more comprehensive evaluation of triple bottom line impacts, a reduction in emissions and resources consumed during site activities, efficient use of financial resources, and a maximization of benefits to stakeholders, in particular the community
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