A new approach for layout optimization in maintenance workshops with safety factors: The case of a gas transmission unit

In this study, an Integrated Simulation-Data Envelopment Analysis (DEA) approach is presented for optimum facility layout of maintenance workshop in a gas transmission unit. The process of repair of incoming parts includes various operations on different facilities. The layout problem in this system involves determining the optimum location of all maintenance shop facilities. Layout optimization plays a crucial role in this type of problems in terms of increasing the efficiency of main production line. Standard types of layouts including U, S, W, Z and straight lines are considered. First, the maintenance workshop is modeled with discrete-event-simulation. Time in system, average waiting time, average machine utilization, average availability of facilities, average queue length of facilities (AL) and average operator utilization are obtained from simulation as key performance indicators (KPIs) of DEA. Also, safety index and number of operators are considered as other KPIs. Finally, a unified non-radial Data Envelopment Analysis (DEA) is presented with respect to the stated KPIs to rank all layouts alternatives and to identify the best configuration. Principle Component Analysis (PCA) is used to validate and verify the results. Previous studies do not consider safety factor in layout design problems. This is the first study that presents an integrated approach for identification of optimum layout in a maintenance workshop of gas transmission unit by incorporating safety and conventional factors.     

Assessing farming eco-efficiency: a Data Envelopment Analysis approach

This paper assesses farming eco-efficiency using Data Envelopment Analysis (DEA) techniques. Eco-efficiency scores at both farm and environmental pressure-specific levels are computed for a sample of Spanish farmers operating in the rain-fed agricultural system of Campos County. The determinants of eco-efficiency are then studied using truncated regression and bootstrapping techniques. We contribute to previous literature in this field of research by including information on slacks in the assessment of the potential environmental pressure reductions in a DEA framework. Our results reveal that farmers are quite eco-inefficient, with very few differences emerging among specific environmental pressures. Moreover, eco-inefficiency is closely related to technical inefficiencies in the management of inputs. Regarding the determinants of eco-efficiency, farmers benefiting from agri-environmental programs as well as those with university education are found to be more eco-efficient. Concerning the policy implications of these results, public expenditure in agricultural extension and farmer training could be of some help to promote integration between farming and the environment. Furthermore, Common Agricultural Policy agri-environmental programs are an effective policy to improve eco-efficiency, although some doubts arise regarding their cost-benefit balance.     

An innovative computer design for modeling forest landscape change in very large spatial extents with fine resolutions

Although forest landscape models (FLMs) have benefited greatly from ongoing advances of computer technology and software engineering, computing capacity remains a bottleneck in the design and development of FLMs. Computer memory overhead and run time efficiency are primary limiting factors when applying forest landscape models to simulate large landscapes with fine spatial resolutions and great vegetation detail. We introduce LANDIS PRO 6.0, a landscape model that simulates forest succession and disturbances on a wide range of spatial and temporal scales. LANDIS PRO 6.0 improves on existing forest landscape models with two new data structures and algorithms (hash table and run-length compression). The innovative computer design enables LANDIS PRO 6.0 to simulate very large (>10⁸ ha) landscapes with a 30-m spatial resolution, which to our knowledge no other raster forest landscape models can do. We demonstrate model behavior and performance through application to five nested forest landscapes with varying sizes (from 1 million to 100 million 0.09-ha cells) in the southern Missouri Ozarks. The simulation results showed significant and variable effects of changing spatial extent on simulated forest succession patterns. Results highlighted the utility of a model like LANDIS PRO 6.0 that is capable of efficiently simulating large landscapes and scaling up forest landscape processes to a common regional scale of analysis. The programming methodology presented here may significantly advance the development of next generation of forest landscape models.     

饵料加标研究二甲苯麝香在鱼体内的蓄积和清除能力

二甲苯麝香(musk xylene,MX)是一种人造麝香,具有强烈的麝香气,可用作化妆品香精和皂用香精等的定香剂。由于其持续不断地输入环境,它们在水、土壤、大气中的浓度逐渐升高,并且在动物和人体组织中产生了蓄积,其蓄积效应相当于有机氯农药(organochlorine pesticides,OCPs)等持久性有机污染物,因而研究MX在鱼体内的蓄积特征是十分必要的。本文用含浓度为50 mg·kg-1和100 mg·kg-1的MX的饵料投喂斑马鱼(Danio rerio)和稀有鮈鲫(Gobiocypris rarus)14 d,继而进入为期28 d的清除实验。通过快速溶剂萃取(accelerated solvent extraction,ASE)、凝胶渗透色谱(gel permeation chromatography,GPC)、气相色谱-质谱联用法(gas chromatography-mass spectrometry,GC-MS)测定斑马鱼和稀有鮈鲫体内MX的浓度,继而评估鱼对MX的清除能力。结果表明,饵料中MX浓度越高,鱼体内MX的蓄积水平越高,100 mg·kg-1浓度组的蓄积水平为50 mg·kg-1浓度组的蓄积水平的2~3倍。在整个试验阶段的不同时间点分析鱼的体重和脂肪与鱼体内MX浓度的关系,数据显示,MX在鱼体内的蓄积水平与鱼的体重存在正线性关系,而与鱼体内脂肪含量的关系还有待进一步研究。经28 d的清除,鱼体内的MX残留量接近于零,说明斑马鱼和稀有鮈鲫对MX的清除能力较强。     

Techno-economic characteristics of wastewater treatment plants retrofitted from the conventional activated sludge process to the membrane bioreactor process

• Retrofitting from CAS to MBR increased effluent quality and environmental benefits. • Retrofitting from CAS to MBR increased energy consumption but not operating cost. • Retrofitting from CAS to MBR increased the net profit and cost efficiency. • The advantage of MBR is related to the adopted effluent standard. • The techno-economy of MBR improves with stricter effluent standards. While a growing number of wastewater treatment plants (WWTPs) are being retrofitted from the conventional activated sludge (CAS) process to the membrane bioreactor (MBR) process, the debate on the techno-economy of MBR vs. CAS has continued and calls for a thorough assessment based on techno-economic valuation. In this study, we analyzed the operating data of 20 large-scale WWTPs (capacity≥10000 m³/d) and compared their techno-economy before and after the retrofitting from CAS to MBR. Through cost-benefit analysis, we evaluated the net profit by subtracting the operating cost from the environmental benefit (estimated by the shadow price of pollutant removal and water reclamation). After the retrofitting, the removal rate of pollutants increased (e.g., from 89.0% to 93.3% on average for NH₃-N), the average energy consumption increased from 0.40 to 0.57 kWh/m³, but the operating cost did not increase significantly. The average marginal environmental benefit increased remarkably (from 0.47 to 0.66 CNY/g for NH₃-N removal), leading to an increase in the average net profit from 19.4 to 24.4 CNY/m³. We further scored the technical efficiencies via data envelopment analysis based on non-radial directional distance functions. After the retrofitting, the relative cost efficiency increased from 0.70 to 0.73 (the theoretical maximum is 1), while the relative energy efficiency did not change significantly. The techno-economy is closely related to the effluent standard adopted, particularly when truncating the extra benefit of pollutant removal beyond the standard in economic modeling. The modeling results suggested that MBR is more profitable than CAS given stricter effluent standards.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Advancing injury and violence prevention through data science

Introduction: The volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative. Method: The Centers for Disease Control and Prevention’s (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others. Results: To achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.     

基层环境统计年报中的问题及建议

环境统计工作是环境保护工作中的基础,是各级政府和环境保护行政主管部门制定环境保护政策和计划、加强环境监督管理和污染防治的重要依据。本文结合日常工作经验,就目前基层环境统计工作中存在的问题进行探讨,并提出一些建议。     

振动试验数据管理系统概述

随着近些年振动试验技术的快速发展,对于振动试验数据的保存、管理和统计提出了更高的要求。鉴于此,附属于试验室信息管理系统的振动试验数据管理系统作为振动试验有效的管理工具,必然会成为提升试验室振动试验能力、规范试验技术的有效工具。     

Spatial modelling of the probability of obtaining a detectable concentration of ammonia in Moreton Bay, Australia

Ammonia is an important water quality variable, which in excess, can be detrimental to waterways and their ecosystems. In the Ecosystem Health Monitoring Program in South-east Queensland ammonia is monitored monthly, however, often more than 50% of the ammonia observations in Moreton Bay are below detection limit, making it difficult to draw useful inferences. In this paper a clipped Gaussian random field is used to spatially model and map the probability of detectable concentration of ammonia. The methodology is applied to the Moreton Bay samples collected in February 2005. The results suggest that for this month the oceanic impacted areas have higher probability of detectable ammonia concentration than the areas closer to the main sources of anthropogenic inputs.