A Robust Optimization Model for an Invasive Species Management Problem

Invasive species pose a significant threat to global biodiversity. Managing invasive species often involves modeling the species’ spread pattern, estimating control costs and damage costs due to the invasion, designing control efforts, and accounting for uncertainties in model parameters. Dealing with uncertainty is arguably the most important part of the process, since biological, environmental, and economic factors can cause parameter values to vary greatly. Managers need decision tools that are robust to such limited or variable information. Here, we present a robust spatial optimization model to select treatment sites in a way that maximally reduces the size of an invasive population, given a constraint on financial resources. We develop an integer programming model that includes population dynamics and management costs over space and time. The model incorporates uncertainty in the available budget and the invasive spread rate as sets of discrete scenarios to determine a robust, cost-effective management plan in a novel way.     

Design sensitivity of reactive absorption units for improved dynamic performance and cleaner production: the NOx removal process

The dynamic characteristics of reactive absorption processes are of great importance for the smooth operation of the unit and the overall performance of the implemented control system under the influence of process disturbances and the presence of tight environmental and safety constraints. In the present study, the effect of the major design parameters and column configurations on the dynamic behaviour of the environmentally sensitive NO_x removal processes, through the use of rigorous rate-based dynamic models, is investigated. Static and dynamic disturbance rejection properties are evaluated for the screening and assessment of alternative design decisions.     

An evaluation of the role of risk-based decision-making in a former manufactured gas plant site remediation

Environmental remediation decisions are driven by the need to minimize human health and ecological risks posed by environmental releases. The Risk Assessment Guidance for Superfund Sites enunciates the principles of exposure and risk assessment that are to be used for reaching remediation decisions for sites under Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Experience with remediation management under CERCLA has led to recognition of some crucial infirmities in the processes for managing remediation: cleanup management policies are ad hoc in character, mandates and practices are strongly conservative, and contaminant risk management occurs in an artificially narrow context. The purpose of this case study is to show how a policy of risk-based decision-making was used to avoid customary pitfalls in site remediation. This case study describes the risk-based decision-making process in a remedial action program at a former manufactured gas plant site that successfully achieved timely and effective cleanup. The remediation process operated outside the confines of the CERCLA process under an administrative consent order between the utility and the New Jersey Department of Environmental Protection. A residential use end state was negotiated as part of this agreement. The attendant uncertainties, complications, and unexpected contingencies were overcome by using the likely exposures associated with the desired end state to structure all of the remediation management decisions and by collecting site-specific information from the very outset to obtain a detailed and realistic characterization of human health risks that needed to be mitigated. The lessons from this case study are generalizable to more complicated remediation cases, when supported by correspondingly sophisticated technical approaches.