An integrated numerical and physical modeling system for an enhanced in situ bioremediation process

Groundwater contamination due to releases of petroleum products is a major environmental concern in many urban districts and industrial zones. Over the past years, a few studies were undertaken to address in situ bioremediation processes coupled with contaminant transport in two- or three-dimensional domains. However, they were concentrated on natural attenuation processes for petroleum contaminants or enhanced in situ bioremediation processes in laboratory columns. In this study, an integrated numerical and physical modeling system is developed for simulating an enhanced in situ biodegradation (EISB) process coupled with three-dimensional multiphase multicomponent flow and transport simulation in a multi-dimensional pilot-scale physical model. The designed pilot-scale physical model is effective in tackling natural attenuation and EISB processes for site remediation. The simulation results demonstrate that the developed system is effective in modeling the EISB process, and can thus be used for investigating the effects of various uncertainties.     

A fuzzy composting process model

Composting processes are normally complicated with a variety of uncertainties arising from incomplete or imprecise information obtained in real-world systems. Previously, there has been a lack of studies that focused on developing effective approaches to incorporate such uncertainties within composting process models. To fill this gap, a fuzzy composting process model (FCPM) for simulating composting process under uncertainty was developed. This model was mainly based on integration of a fractional fuzzy vertex method and a comprehensive composting model. Degrees of influence by projected uncertain factors were also examined. Two scenarios were investigated in applying the FCPM method. In the first scenario, model simulation under deterministic conditions was conducted. A pilot-scale experiment was provided for verifications. The result indicated that the proposed composting model could provide an excellent vehicle for demonstrating the complex interactions that occurred in the composting process. In the second scenario, application of the proposed FCPM was conducted under uncertainties. Six input parameters were considered to be of uncertain features that were reflected as fuzzy membership functions. The results indicated that the uncertainties projected in input parameters will result in significant derivations on system predictions; the proposed FCPM can generate satisfactory system outputs, with less computational efforts being required. Analyses on degree of influence of system inputs were also provided to describe the impacts of uncertainties on system responses. Thus, suitable measures can be adopted either to reduce system uncertainty by well-directed reduction of uncertainties of those high-influencing parameters or to reduce the computational requirement by neglecting those negligible factors.     

IPCS: an integrated process control system for enhanced in-situ bioremediation

To date, there has been little or no research related to process control of subsurface remediation systems. In this study, a framework to develop an integrated process control system for improving remediation efficiencies and reducing operating costs was proposed based on physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks. Process control for enhanced in-situ bioremediation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. Application of the proposed approach to a bioremediation process in a pilot-scale system indicated that it was effective in dynamic optimization and real-time process control of the sophisticated bioremediation systems.     

Fate and contaminant transport model-driven probabilistic human health risk assessment of DNAPL-contaminated site

In this study, fate and contaminant transport model-driven human health risk indexes were calculated due to the presence of dense non-aqueous phase liquids (DNAPLs) in the subsurface environment of air force base area in Florida, USA. Source concentration data of DNAPLs was used for the calculation of transport model-driven health risk indexes for the children and adult sub-population via direct oral ingestion and skin dermal contact exposure scenario using 10,000 Monte Carlo type simulations. The highest variation in the probability distribution of transformed DNAPL compound (cis-dichloroethene (cis-DCE) > vinyl chloride (VC)) was observed as compared to parent DNAPL (tetrachloroethene (PCE)) based on the 50-year simulation timespan. Transformed DNAPL compounds (VC, cis-DCE) posed the highest risk to human health for a longer duration (up to 15 years) in comparison to parent DNAPL (PCE), as non-carcinogenic hazard quotient varied from 400 to 1100. Carcinogenic health risks were observed as 3-order of magnitude higher than safe limit (HQ_Safe < 10⁻⁶) from 2nd to 5th year timespan and fall in the high-risk zone, indicating the need for a remediation plan for a contaminated site. Variance attribution analysis revealed that concentration, body weight, and exposure duration (contribution percentage – 70 to 95%) were the most important parameters, highlighting the impact of dispersivity and exposure model in the estimation of risk indexes. This approach can help decision-makers when a contaminated site with partial data on hydrogeological properties and with higher uncertainty in model parameters is to be assessed for the formulation of remediation measures.