超滤工艺水质模型研究进展

超滤在水处理行业得到日益广泛的应用,超滤工艺水质模型的研究对于理解过程机理和优化工艺运行具有重要意义。综述了超滤对溶质截留模型的研究进展,介绍了主要机理模型和经验模型的建模理论和方法、关键方程、主要参数等,并侧重从水处理行业适用性的角度评价了现有超滤工艺水质模型研究的不足,包括:以模拟单一溶质体系的模型为主、基于生产规模实际系统的模型极少、针对水处理行业的模型较少等。为了适应水处理行业中超滤工艺发展的需求,今后应加强超滤工艺过程机理的基础研究,探索将机理模型和经验模型相结合,开发更为有效实用的超滤工艺水质模型。     

某化工园区地下水污染模拟研究

文章以某化工园区为对象,基于现场水文地质条件和GMS(Groundwater Modeling System)软件分别建立了地下水流概念模型和数值模型以及溶质运移数值模型,模拟了不同类型污染物的污染路径和污染羽迁移状况,分析了污染对地下水环境可能造成的影响。结论可知:污染物沿污染中心向四周浓度逐渐减小,浓度梯度较大;污染物在含水层中会沿地下水流方向呈近似椭圆形状运移,且在水流的反方向亦有扩散现象存在;同一模拟时段内,COD不会超标,氨氮在第二水处理站的浓度中心处随时间迁移而超标,但超标浓度和范围不明显;模拟时段内,所有污染物均未超出厂界从而抵达长江,故不会影响长江水质。     

反渗透脱盐水处理技术在沙特南方省水泥公司水泥生产线上的尝试与探索

由中材国际总包的沙特南方省水泥公司5000t/d熟料水泥生产线,采用了反渗透脱盐水处理技术处理当地苦咸水,这是有别于国内项目的重要部分之一。文章介绍了该项目从进、出水质设计控制指标,我国及美国《反渗透水处理设备》标准中对水质的要求及饮用水水质标准,水处理工艺流程,设备选型及水处理药品建议。工程运行效果表明:该项目的水处理系统获得成功,处理后的水质能完全达到设计要求。     

鸡西矿业集团矿井水处理利用现状

介绍了鸡西矿业集团矿井水处理利用原则及水质状况,根据用户对水质的不同要求,采取不同的矿井水处理工艺,矿井水利用率达到86%,取得了较好的经济效益和环境效益。     

多喷嘴水煤浆气化技术渣水处理工艺介绍

渣水处理是水煤浆气化工艺的主要工序之一,承担着水质处理的重要功能。文章从工艺流程、水质控制、正常操作、开停车注意事项四方面对渣水处理工序进行了介绍。     

大型板带热轧水处理系统节水设计探讨

大型板带热轧水处理系统是热轧主轧线的配套设施，新水用量大、废水排量大、浓缩倍数低。结合某大型板带热轧水处理系统，从循环水量和水质、水处理工艺、各系统排污水量和水质、各系统补充水量和水质、系统浓缩倍数等方面进行了深入分析，就大型板带热轧水处理系统节水设计进行了探讨，可供类似项目设计或工程运维做参考。     

临涣电厂循环冷却水处理中的pH控制

水处理药剂可抑制工业循环水系统结垢,但是对于一些高硬度、高碱度、高硫酸根的水质,仅仅依靠水处理药剂往往达不到预期的效果.可以通过调节pH改善阻垢效果.作者针对临涣电厂水质进行了一系列的试验,探究了pH对水处理药剂阻垢能力的帮助.     

日本冷却塔循环水的水质管理标准

<正> 2.冷却塔循环水的水质控制范围:在不使用水处理剂的情况下,希望按表—1的标准进行维护管理,作为简易的水质控制指标,可以使用pH值与电气传导率的水质状态作为各种水质等级的区分检验。 (1)不使用水处理剂情况下的水质控制     

气化工艺水系统管理及技改优化总结

正气化系统的水质好坏受煤质、药剂添加、渣水处理相关设备运行状况等诸多条件影响,只能通过水质分析数据来了解系统水质情况,因此,需要在平时做好水处理药剂添加、稳定渣水系统三级闪蒸工艺装置的运行和水处理附属设备的日常维护工作,以保证系统水质稳定。1气化水系统设计情况河南心连心化肥有限公司气化装置采用华东理工大学和兖矿集团共同开发的新型水煤浆加压气化技术,装置包括3台操作压力为6.5 MPa的气化炉,2开1备。渣水处理系统采用三级闪蒸     

基于CE-QUAL-W2模型的地表径流对滴水湖水质影响模拟

通过二维纵向横向平均水动力学和水质模型(CE-QUAL-W2模型)来模拟滴水湖的水质状况。模型初步模拟了滴水湖中不同深度水层的水质状况,使用2010年3月到2010年10月的数据对模型参数进行校正,实验期间采集到的地表径流数据作为入流源。模拟显示,最差的水质发生在最大降雨量的月份。预测的综合滴水湖水质是Ⅱ～Ⅳ类。TP和TN水质水平较差,TP都大于Ⅲ类,尤其在6月为V类。TN在整个研究时段浓度都较高,大部分都大于Ⅳ类。水质状况的模型计算值与实测值接近,与实际情况吻合。     

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented.     

Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

活性污泥数学模型在污水处理中的应用研究

简要介绍了IWA专家组提出的4个活性污泥数学模型:ASM1、ASM2、ASM2D和ASM3。从污水处理工艺的改造、优化运行和工艺研究三个方面,综述了ASMs系列模型在国内外的应用状况。     

一种改进的热泵供暖系统动态实时优化策略

基于经济性目标的热泵供暖动态优化操作具有重要意义，但在操作区间内环境温度和模型参数变化的不确定性会对实际优化操控带来很大挑战。在完善热泵供暖系统模型的基础上，提出了一种改进的动态实时优化控制策略以改善系统的实际节能效果。该方法首先建立以压缩机和送水泵运行频率为控制变量的热泵供暖系统的非线性动态关系模型，并得到以24 h为周期、以综合性能指标最低为目标的动态实时优化命题。然后，在给定24 h环境温度预测情况下通过求解该优化命题得到热泵压缩机和送水泵的最优运行频率轨线，并以当前时间点的最优控制量对热泵供暖系统进行控制；接着，基于天气逐时预测和模型参数最新校验结果对环境温度轨线或者模型参数进行更新，不断地求解原优化命题以更新最优控制轨线，并不断地采用当前点的最优控制量对热泵供暖系统进行控制，直到当前时间点达到第24 h。实例计算结果表明：采用本文提出的方法可以进一步改善热泵供暖系统的动态优化操控效果，并能够很好地满足给定终端约束要求。本方法对于具有周期性和不确定参数的动态实时优化问题求解具有一定的借鉴意义。     

底水油藏水平井开发适用性优化研究

水平井技术主要是因射孔井段在油藏中平行于油藏顶底界面延伸,增大了与油藏的接触面,降低生产压差,提高生产井产能。特别是底水油藏由于经长时间开采后,油水关系较为复杂,油水界面存在波状起伏现象,水平井技术是开发底水油藏的有效技术手段。为了充分发挥水平井的优越性,对确定水平井水平段位置及方向、水平段长度等参数进行优化研究,为经济有效动用同类油藏具有指导意义。     

Profit optimization for chemical process plant based on a probabilistic approach by incorporating material flow uncertainties

This paper reports how the economic performance of a chemical process plant is affected by material flow uncertainties from the plant inlet and outlet. Two chance-constrained optimization models were proposed. The models were tested using case studies of an existing gas processing plant. Profit optimization for the case studies was made with respect to the reliability of holding the process constraints at a certain confidence level [0.5, 1]. The optimal profit change for uncertainty from the plant inlet within the confidence interval [0.96, 1] was 86%. On the other hand, the optimal profit change for uncertainty from the plant outlet was only 2% for the same confidence level interval considered. This suggests that the uncertainty from the plant inlet has a major impact on the overall economic performance of the plant. Sensitivity analysis showed how uncertain parameters from both plant sides can affect the overall profit significantly.     

氯化及预氯化反应条件与卤代烃生成研究

以巢溯源水为源水,进行了加氯量、不同工艺段加氯、反应时间、源水中有机物含量、pH等氯化反应条件对窿代烃生成量影响约实验,并在此基础上,对巢湖源水和董铺源水进行了生产试验,提出了给水处理控制预氯量、优化常规处理、进行深度处理、降低三卤甲烷等消毒剿产物生成量的控制对策。     

PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.     

PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.