绿色环保、高效节能、资源循环利用的清洁炼钢厂

主要介绍宝钢集团浦钢搬迁罗泾工程为建设绿色环保、高效节能、资源循环利用的现代清洁炼钢厂所采用的工艺技术、技术装备和技术措施。对炼钢工程中所采用的各种工艺技术、技术装备和技术措施等进行说明和分析。     

Bayesian Networks and Adaptive Management of Wildlife Habitat

Abstract: Adaptive management is an iterative process of gathering new knowledge regarding a system's behavior and monitoring the ecological consequences of management actions to improve management decisions. Although the concept originated in the 1970s, it is rarely actively incorporated into ecological restoration. Bayesian networks (BNs) are emerging as efficient ecological decision‐support tools well suited to adaptive management, but examples of their application in this capacity are few. We developed a BN within an adaptive‐management framework that focuses on managing the effects of feral grazing and prescribed burning regimes on avian diversity within woodlands of subtropical eastern Australia. We constructed the BN with baseline data to predict bird abundance as a function of habitat structure, grazing pressure, and prescribed burning. Results of sensitivity analyses suggested that grazing pressure increased the abundance of aggressive honeyeaters, which in turn had a strong negative effect on small passerines. Management interventions to reduce pressure of feral grazing and prescribed burning were then conducted, after which we collected a second set of field data to test the response of small passerines to these measures. We used these data, which incorporated ecological changes that may have resulted from the management interventions, to validate and update the BN. The network predictions of small passerine abundance under the new habitat and management conditions were very accurate. The updated BN concluded the first iteration of adaptive management and will be used in planning the next round of management interventions. The unique belief‐updating feature of BNs provides land managers with the flexibility to predict outcomes and evaluate the effectiveness of management interventions.     

基于GIS的矿井瓦斯赋存规律多影响因素智能分析技术

为了提高矿井瓦斯赋存规律分析的准确性和智能化程度,提出了多因素共同作用下瓦斯原始数据筛选和瓦斯赋存规律分析的一种智能化技术。介绍了如何利用地理信息（GIS）技术建立瓦斯地质空间数据模型,结合计算机编程实现了对大量复杂的原始数据的智能分析、处理和筛选。并实现了瓦斯赋存规律多种主控因素情况下多元回归的自动智能化分析,进而使得瓦斯赋存规律模型可以在取得新的原始数据后进行动态、智能的更新。为煤矿工作人员快速、准确的掌握地矿井瓦斯赋存规律提供了先进的手段和工具。     

提高循环水浓缩倍数节水减排方案探讨

针对川西北某轻烃厂循环冷却水水质的特点,拟采用无磷药剂配方将循环水浓缩倍数从3．0提高到5．0,并新增软水处理系统作为循环水补水,从而达到优化水质、节水减排的目的。针对浓缩倍数提高引起得的水质稳定难题提出相应的技术方案,最终达到改善水质,保护水环境,节水减排的效果。     

高危行业事故应急科技服务实用性平台研究

针对当前我国高危行业应急平台建设中存在事故应急救援决策支撑弱、应急救援服务能力差的缺陷,以“江苏省高危行业事故应急科技服务平台”项目为背景,提出高危行业事故应急科技服务平台的总体建设架构;并以此为基础提出建立应急信息共享,预案可视化与推演,事故后果分析模拟,应急救援决策支持,应急咨询、培训、评估,事故现场快速监测六大子平台。以期利用VR、GIS、人工智能等先进技术,从功能、系统、体系三个层面完善高危行业事故应急科技服务平台建设内容,从而为政府门提供应急决策支持、为企业和社会公众提供应急科技服务。     

制药废水采用微生物絮凝剂和粉煤灰过滤预处理的初步试验研究

初步研究一种用于高浓度制药废水的预处理工艺方法。采用微生物絮凝剂和粉煤灰过滤相结合的预处理工艺,其综合的效果可以将高浓度制药废水中的COD去除80%,基本脱色澄清,且可以将对生化处理有抑制作用的抗生素效价予以降低。并初步讨论了该工艺在实际生产中的可行性。     

论述造纸行业控制可吸附有机卤化物的必要性

造纸业漂白过程会带来可吸附有机卤化合物的污染,这是一类含有包括二恶英在内的多种有毒有机物。随着全球贸易进程,控制造纸业可吸附有机卤化合物的排放已经刻不容缓。控制造纸行业可吸附有机卤化物应从工艺技术、排放标准执行、监测等方面着手。     

基于知识的洞庭湖湿地遥感分类方法

湿地遥感影像分类是遥感研究的一大难题。分析洞庭湖不同湿地类型在遥感影像上的光谱曲线规律,利用两个季节的洞庭湖ETM数据,并辅助以物候特征和地面GIS信息,通过遥感软件Erdas Image的专家分类知识库建立决策树分类方法,结合研究区的DEM进行洞庭湖湿地的影像分类,通过专家分类器分层次实现了包括水体、泥沙滩地、防护林滩地、湖草、芦苇滩地和苔草滩地以及其他水体7种湿地类型的分类。相比传统分类方法,专家分类过程以规则为基础,可以同时利用多个条件进行分类,减少了数据处理时间,同时还提高了分类精度,最终得到试验区较为可靠的遥感分类图像。     

Decision support for sustainable manufacturing using decision guidance query language

Sustainability has become a very significant research topic as it impacts many different manufacturing industries. Therefore, the technologies for monitoring, analysing, evaluating and optimising the sustainability performance of manufacturing processes and systems are very critical for decision makers on the shop floor. This paper introduces a decision guidance management system that provides actionable recommendations through quantitative analysis of the sustainability measures of manufacturing processes and systems based on life cycle assessment. The system determines decision preferences through dynamically collected data and decision makers' responses, taking into account the prevailing constraints. Optimal decisions can be derived using mathematical and constraint programming. By using decision guidance query language, this methodology allows users to make optimal decisions without an extensive mathematical or operations research background. Knowledge of relational databases is sufficient for a user to formulate the optimisation problem and obtain optimal solutions. The methodology is demonstrated with a machining operation case study, in which a list of sustainability metrics are identified and sustainability modelling methods are proposed. Important sustainable machining performance measures are optimised, resulting in actionable recommendations.     

Passive and active adaptive management: approaches and an example

Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted.