Biomedical subjects

Eric Davila

Publications and source records attributed to Eric Davila.

6 recordsLinked to original sources

PubMed2006

Siting and routing assessment for solid waste management under uncertainty using the grey mini-max regret criterion.

Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational and data-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public-private partnerships may alleviate this obstacle.

Computer Simulation↗

PubMed2006

Minimax regret optimization analysis for a regional solid waste management system.

Solid waste management (SWM) facilities are crucial for environmental management and public health in urban regions. Due to the waste management hierarchy, one of the greatest challenges that organizations face today is to figure out how to diversify the treatment options, increase the reliability of infrastructure systems, and leverage the redistribution of waste streams among incineration, compost, recycling, and other facilities to their competitive advantage region wide. Systems analysis plays an important role for regionalization assessment of integrated SWM systems, leading to provide decision makers with break-through insights and risk-informed strategies. This paper aims to apply a minimax regret optimization analysis for improving SWM strategies in the Lower Rio Grande Valley (LRGV), an economically fast growing region in the US. Based on different environmental, economic, legal, and social conditions, event-based simulation in the first stage links estimated waste streams in major cities in LRGV with possible solid waste management alternatives. The optimization analysis in the second stage emphasizes the trade-offs and associated regret evaluation with respect to predetermined scenarios. Such optimization analyses with multiple criteria have featured notable successes, either by public or private efforts, in diverting recyclables, green waste, yard waste, and biosolids from the municipal solid waste streams to upcoming waste-to-energy, composting, and recycling facilities. Model outputs may link prescribed regret scenarios in decision making with various scales of regionalization policies. The insights drawn from the system-oriented, forward-looking, and preventative study can eventually help decision-makers and stakeholders gain a scientific understanding of the consequences of short-term and long-term decisions relating to sustainable SWM in the fast-growing US-Mexico borderland.

Conservation of Natural Resources↗

PubMed2005

Landfill space consumption dynamics in the Lower Rio Grande Valley by grey integer programming-based games.

The Lower Rio Grande Valley (LRGV) region in South Texas emerges as a warehouse and transportation center between Central America and the US with positive growth impacts due to the North American Free Trade Agreement (NAFTA). In 10 years time, a 39.8% population increase has resulted in a 25% boost in solid waste per capita disposal rate in the region. A landfill space shortage drives a need for landfill operators to understand their optimal management strategies in this highly-competitive market. Initially, a strategic plan for optimal solid waste pattern distribution minimizes net costs for cities. This is accomplished through a grey integer programming algorithm that encapsulates all uncertainty present in the solid waste system. Secondly, a series of grey integer submodels construct payoff matrices for a zero-sum two-person game. The ensuing game theoretic analysis is critical for evaluating optimal pricing strategies for tipping fees available to the most significant regional landfills (e.g. Browning-Ferris Industries (BFI) and City of Edinburg) as they compete over disposal contracts. The BFI landfill intrinsically benefits from its competitive pricing policy and central location to solid waste generators. The City of Edinburg landfill, on the other hand, wishes to secure its lucrative solid waste management revenue. It desires a gaming strategy backed by optimality that integrates ambiguity in solid waste generation, design capacity boundaries, and unitary shipping costs. Results show that a two-tiered analysis via grey integer programming-based games may pave the way for 'grey Nash equilibria' pricing tactics that will help the Edinburg landfill maintain its waste contracts.

Algorithms↗

PubMed2005

Sustainable pattern analysis of a publicly owned Material Recovery Facility in a fast-growing urban setting under uncertainty.

Sustainable development goals are achievable through the installation of Material Recovery Facilities (MRFs) in certain solid waste management systems, especially those in rapidly expanding multi-district urban areas. MRFs are a cost-effective alternative when curbside recycling does not demonstrate long-term success. Previous capacity planning uses mixed integer programming optimization for the urban center of the city of San Antonio, Texas to establish that a publicly owned material recovery facility is preferable to a privatized facility. As a companion study, this analysis demonstrates that a MRF alleviates economic, political, and social pressures facing solid waste management under uncertainty. It explores the impact of uncertainty in decision alternatives in an urban environmental system. From this unique angle, waste generation, incidence of recyclables in the waste stream, routing distances, recycling participation, and other planning components are taken as intervals to expand upon previous deterministic integer-programming models. The information incorporated into the optimization objectives includes economic impacts for recycling income and cost components in waste management. The constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening restrictions. Due to the fragmented data set, a grey integer programming modeling approach quantifies the consequences of inexact information as it propagates through the final solutions in the optimization process. The grey algorithm screens optimal shipping patterns and an ideal MRF location and capacity. Two case settings compare MRF selection policies where optimal solutions exemplify the value of grey programming in the context of integrated solid waste management.

Algorithms↗

PubMed2005

Optimal design for sustainable development of a material recovery facility in a fast-growing urban setting.

Installing material recovery facilities (MRFs) in a solid waste management system could be a feasible alternative to achieve sustainable development goals in urban areas if current household and curbside recycling cannot prove successful in the long run. This paper addresses the optimal site selection and capacity planning for a MRF in conjunction with an optimal shipping strategy of solid waste streams in a multi-district urban region. Screening of material recovery and disposal capacity alternatives can be achieved in terms of economic feasibility, technology limitation, recycling potential, and site availability. The optimization objectives include economic impacts characterized by recycling income and cost components for waste management, while the constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening constraints. A case study for the City of San Antonio, Texas (USA) presents a vivid example where scenario planning demonstrates the robustness and flexibility of this modeling analysis. It proves especially useful when determining MRF ownership structure. Each scenario experiences two case settings: (1) two MRF sites are proposed for selection and (2) a single MRF site is sought. Cost analysis confirms processing fees are not the driving force in the City's operation, but rather shipping cost. Sensitivity analysis solidifies the notion that significant public participation plays the most important role in minimizing solid waste management expenses.

Cities↗

PubMed2005

Interactive analysis of waste recycling and energy recovery program in a small-scale incinerator.

Conflicting goals affecting solid waste management are explored in this paper to find the best implementation of resource recovery with a small-scale waste-to-energy process. Recycling paper and plastic material often leaves a shortage of thermal energy to support incineration that forces operators to supplement the process with auxiliary fuels. Although there are considerable profits to be made from material recovery, the increase of fuel usage causes conflict given that it is cost prohibitive. A series of trials performed on a small-scale 1.5-t/day incineration plant with a cyclone heat recovery system found that material recycling can impede performance. Experimental results are expressed as empirical regression formulas with regard to combustion temperature, energy transfer, and heat recovery. Process optimization is possible if the waste moisture content remains <30%. To test the robustness of the optimization analysis, a series of sensitivity analyses clarify the extent of material recycling needed with regard to plastic, paper, and metal. The experiments also test whether the moisture in the waste would decrease when recycling paper because of its exceptional capacity to absorb moisture. Results show that recycling paper is strongly recommended when the moisture content is >20%, whereas plastic recycling is not necessary at that moisture condition. Notably, plastic recovery reduces the heat needed to vaporize the water content of the solid waste, thus it is recommended only when the moisture content is <10%. For above-normal incineration temperatures, plastic recycling is encouraged, because it removes excess energy. Metal is confirmed as an overall priority in material recycling regardless of the moisture content of the incoming waste.

Conservation of Energy Resources↗