炼油厂活性污泥中硝化细菌富集条件优化及效果评价

为提高炼油厂废水中氨氮化合物的去除效率．对生物曝气池活性污泥中的硝化菌群进行培养条件优化和并富集培养。实验结果表明,在pH值为8．0,28℃环境,150r／min振荡暗培养条件下,通过不断添加富集培养基,经过6周培养后,可使湿泥中硝化细菌的数量增加24．95倍／g,硝化速率提高2．76倍,达到了富集的目的。     

新型城市污水脱氮除磷工艺的试验研究与优化设计

提出了一种改进型A2/O脱氮除磷工艺(MAAO),通过工艺试验和活性污泥2D模型对其进行运行和设计优化.结果表明,系统对COD、TN、NH 4-N和TP的平均去除率分别为85.7%、66.8%、97.35%和78.1%;当COD在300mg/L以上时,脱氮除磷效果均较好,可分别达到70%和90%以上.基于试验结果建立的MMAO工艺数学模型仿真效果良好,可对各单元组合进行优化,校核预测不利工况下的工艺运行效果,实现工艺不同工况下的运行参数优化,较好地实现新工艺的机理性优化设计和运行. MMAO稳定运行出水可达到<城镇污水处理厂污染物排放标准>(GB 18918-2002)一级B标准.由于工艺省去内回流,增加厌氧混合液部分超越提供反硝化碳源,使得运行费用明显降低;系统总停留时间比目前传统的以去除有机污染物为主的生物处理工艺略低,该工艺非常适用于对不具有脱氮除磷功能污水厂的改造.     

A mathematical approach for retrofit and optimization of total site steam distribution networks

This paper presents a generic mathematical model for retrofitting the steam power plants in an industrial site. The industrial sector under review consists of one steel mill, one oil refinery, and several petrochemical plants, where only small-scale steam integration has been implemented before this study. The relevant unit models in a typical steam power plant are established, and the steam plant retrofit problem is formulated as a mixed-integer nonlinear program (MINLP). Feasible retrofit alternatives suggested by experienced field engineers are investigated in sequence to examine the revenue of those possible modifications. The first scenario examines operational optimization of existent plants; the second option allows installation of one new turbine and replacement of several boilers and turbines with lower efficiency; the third scenario considers using a steam ejector to upgrade the disqualified import steam in the oil refinery. The significant merits from these three retrofit alternatives show that the proposed MINLP formulation has been a great help to enhance the inter-plant steam integration in an industrial sector.     

Fuzzy optimization of multi-period carbon capture and storage systems with parametric uncertainties

Carbon capture and storage (CCS) is an important technology option for reducing industrial greenhouse gas emissions. In practice, CO₂ sources are easy to characterize, while the estimation of relevant properties of storage sites, such as capacity and injection rate limit (i.e., injectivity), is subject to considerable uncertainty. Such uncertainties need to be accounted for in planning CCS deployment on a large scale for effective use of available storage sites. In particular, the uncertainty introduces technical risks that may result from overestimating the limits of given storage sites. In this work, a fuzzy mixed integer linear program (FMILP) is developed for multi-period CCS systems, accounting for the technical risk arising from uncertainties in estimates of sink parameters, while still attaining satisfactory CO₂ emissions reduction. In the model, sources are assumed to have precisely known CO₂ flow rates and operating lives, while geological sinks are characterized with imprecise fuzzy capacity and injectivity data. Three case studies are then presented to illustrate the model. Results of these examples illustrate the tradeoff inherent in planning CCS systems under parametric uncertainty.     

Optimization of a Wind/PV Hybrid Power Generation System

This study investigates the wind and solar electricity generation availability at the Solar Energy Institute of Ege University, Izmir, Turkey. The main purpose of this study is to design an appropriate wind-PV hybrid system to cover the electricity consumption of the Institute. In order to do this, monthly average solar irradiation and wind speed data are used, which were measured, consisting of hourly records over an eight-year period from 1995–2002. Simple models were developed to determine wind, solar, and hybrid power resources per unit area. Correlations between the solar and wind power data were carried out on an hourly, daily, and monthly basis. It is shown that the hybrid system can be applied for the efficient and economic utilization of these resources.     

FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management

Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk.     

Inverse calculation approaches for source determination in hazardous chemical releases

Source determination is vital in decision making and emergency planning involving hazardous chemical releases. This work was concentrated on inverse calculation approaches for source determination as well as current trends and future perspectives. In this paper, these different approaches are reviewed by dividing them into two categories: probability modeling methods and optimization modeling methods. The traits of these approaches are comparatively analyzed. Then it is shown how these approaches behave when applied to practical cases, and their feasibility, applicability, stability, and limitation in determining the location and strength are presented. It is argued that when experimenting with potential terrorist attacks involving hazardous chemical releases, observation points should be around the main line of the downwind direction when the source is known; while the uniform distribution of observation points is an efficient solution for unknown incidents. Probability modeling methods are demonstrated to be insufficient during emergency responses due to their lacking of enough prior information of unknown parameters, while optimization modeling methods are efficient and become a new trend in source determination. Findings reflect an urgent need for the development of high-accuracy detectors and further research of data transmission techniques in order to ensure the validity of these approaches.     

Optimal biomass allocation in heterogeneous environments in a clonal plant—Spatial division of labor

When interconnected ramets of clonal plants are growing in heterogeneous environments, ramets may specialize to uptake locally abundant resources rather than scarce resources. This biomass allocation pattern may result in more efficient sharing of resources through physiological integration and an overall benefit to the plants (spatial division of labor; DoL).     

模糊识别在水质综合评价中的应用

应用模糊数学方法对水质进行综合评价,其关键问题是如何合理地确定各评价指标的权重．将最优权法引入模糊评价以确定各评价指标的权系数．通过求解权向量的最优解来确定各指标权重,可避免确定评价指标权重的主观任意性,增加评价结果的分辨性．实例计算与结果对比表明,该方法克服了各指标的权系数随实测样本的不同而变化和评价结果分辨性差的缺点,提高了模型的通用性和评价结果的可比性,同时也简化了多次重复确定权系数的工作量．