SOIL EROSION AND GROUND WATER POLLUTION TRADEOFFS FOR NONIRRIGATED FARMING SYSTEMS1

ABSTRACT: Traditional focus on reducing one environmental externality may cause another externality to increase. This article examines the environmental and economic costs of abating soil loss and (or) nitrate leaching through alternative optimal production systems in the nonirrigated farming systems of Northeastern Oregon. Models estimating soil loss and nitrate‐nitrogen leaching rates associated with current production processes, are linked to a Multi‐Objective Programming (MOP) model. The results show that site specific conditions influence the level of abatement expenditures and optimal production strategies to reduce soil loss and leaching rates. Moreover, while existing production strategies are effective in reducing soil loss at little cost, no strategies could be identified to reduce nitrate leaching rate on some soils.     

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.     

校园网建设的前期规划研究

本文对建设校园网的前期准备工作作了详细的阐述,对各阶段的工作详细作了规划,对于准备建设校园网的学校具有指导意义.     

A Routing and Scheduling System for Infectious Waste Collection

Most hospitals and clinics in Taiwan do not have on-site treatment facilities for their infectious waste and must rely on outside agencies for their collection and treatment. The problem of optimally routing and scheduling the collection of medical waste from a disperse group of facilities is recognized as a periodic vehicle routing problem. This study develops a computer system to solve the resulting optimization problem based on a two-phased approach proposed earlier. The first phase solves a standard vehicle routing problem to determine a set of individual routes for the collection vehicles. The second phase uses a mixed integer programming method to assign routes to particular days of the week. The computer system is user-friendly and consists of several Visual Basic programs while the geographical information system is incorporated to facilitate input and output interface and database management. An illustrative example for the infectious waste of 348 hospitals in the Tainan City area demonstrates the effectiveness of the system.     

A GIS-based Study for Optimizing the Total Emission Control Strategy in Lanzhou City

A geographic information system (GIS)-based approach with spatial analysis advantages was developed to optimize the total emission control scheme on air pollution in Lanzhou, combined with Models-3 modeling system and a linear programming model. GIS system is mainly used for establishing a multifactor assessment model to quantitatively divide environmental functional zone, and also used for selecting control sites in linear programming model. The results show that most of the urban area belongs to the second type of function, which is 122.8 km² occupying about 76.5% and the third type of about 32.9 km² occupying 20.5%. To reach the air quality up to the national standard in the entire control area, some large-emission factories need to cut emissions from 19% to 27%. For small but low-emission height sources, the cut may be as high 40%. The improvement of the ecological environment is urgently needed for controlling the background particle pollution in Lanzhou city.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

SLFP: a stochastic linear fractional programming approach for sustainable waste management

A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.     

Approaches to eliminate waste and reduce cost for recycling glass

In recent years, the issue of environmental protection has received considerable attention. This paper adds to the literature by investigating a scheduling problem in the manufacturing of a glass recycling factory in Taiwan. The objective is to minimize the sum of the total holding cost and loss cost. We first represent the problem as an integer programming (IP) model, and then develop two heuristics based on the IP model to find near-optimal solutions for the problem. To validate the proposed heuristics, comparisons between optimal solutions from the IP model and solutions from the current method are conducted. The comparisons involve two problem sizes, small and large, where the small problems range from 15 to 45 jobs, and the large problems from 50 to 100 jobs. Finally, a genetic algorithm is applied to evaluate the proposed heuristics. Computational experiments show that the proposed heuristics can find good solutions in a reasonable time for the considered problem.     

发展中国家（地区）风电装机规划及我国风电发展重点建议

在全球风电市场稳步发展的背景下,发展中国家（地区）的风电市场也都呈现快速发展的趋势,在各国风电发展持续升温的前提下,未来我国风电发展潜力巨大。列举了非洲、印度、拉丁美洲、东欧、中东等5个发展中国家（地区）未来风电发展规划情况,在总结这些国家或地区风电发展特点的基础上,对我国未来风电发展提出建议。     

Lake eutrophication management modeling using dynamic programming

Lake eutrophication problems have received considerable attention in Taiwan, especially because they relate to the quality of drinking water. In this study, steady-state river water quality and lake eutrophication models are solved using dynamic programming algorithms to find the nutrient removal rates for eutrophication control during dry season. The kinetic cycle of chlorophyll-a, phosphorus and nitrogen for a complete-mixed lake is considered in the optimization framework. The Newton-iterative technique is adopted to solve the nonlinear equations for the steady-state lake eutrophication model. The optimization framework is applied to Cheng-Ching Lake in southern Taiwan. Several nutrient loading scenarios for eutrophication control are studied. Optimization results for nutrient removal rates and corresponding wastewater treatment capacities of each reach of the Kao-Ping River define the least cost approach to lake eutrophication control. A natural purification method, structural free water surface wetland, is also suggested to save more investment and improve river water quality at the same time.