Data mining to support anaerobic WWTP monitoring

The stable and efficient operation of anaerobic wastewater treatment plants (WWTPs) is a major challenge for monitoring and control systems. Support for distributed anaerobic WWTPs through remotely monitoring their data was investigated in the TELEMAC framework. This paper describes how the accumulating filtered sensor data was mined to contribute to the refining of expert experience for insights into digester states. Visualisation techniques were used to present cluster analyses of digester states. A procedure for determining prediction intervals is described together with its application for volatile fatty acid concentrations; this procedure enables prediction risk assessment.     

“五位一体”庭院生态模式

在贵州高海拔地区采用玉米高产栽培措施 ,获得了 12 .2 1t/hm2 的玉米产量 ,超过当地平均产量 2 .7倍 ,钾肥配施可增产玉米 0 .83～ 2 .5 8t/hm2 ,增幅 8.81～ 2 6.80 % ,施钾肥产投比可达 5 .1～ 6.1。施钾肥后玉米的品质有明显的改善 ,试验认为N∶K2 O比为 10 0 :75～ 113 ,即施氮 (N ) 2 0 0kg/hm2 ,施钾 (K2 O) 15 0～ 2 2 5kg/hm2 为宜。     

Grids in control of anaerobic wastewater treatment plants: Leveraging the knowledge

A potential application of Grids systems is to anaerobic wastewater treatment plants (WWTP). This work extends previous simulation and data mining with a laboratory scale anaerobic digester, and shows that extending to an industrial scale plant gives valid and useful results for monitoring and control. The idea of a telecontrol centre for managing a group of plants is gaining acceptance. We investigate what barriers arise in applying data mining across multiple plants, in a Grids environment. One issue is heterogeneities, implying that data mining must be enhanced with the use of metadata. We examine the heterogeneities, link them to the model of Grids, and investigate their implications for metadata representation.     

Control structure selection and model predictive control of the Weyerhaeuser digester problem

The Weyerhaeuser digester problem (WDP) is a simplified digester model which captures the major dynamic characteristics of a continuous pulp digester and has been offered to the research community as an industrial challenge problem. The control objective is to minimize variations in the Kappa # for unmeasured disturbances. A systematic approach to selecting the manipulated inputs and secondary measurements using robust control analysis tools has been successfully applied to the WDP. A multi-rate data-sampling linear model predictive controller using the selected input/measurement pairings provided robust closed-loop performance for the rejection of both deterministic and stochastic unmeasured disturbances.     

Experience with data mining for the anaerobic wastewater treatment process

Anaerobic digestion provides an effective way of disposing organic material in wastewater. The EU-funded TELEMAC project aims at improving the reliability and efficiency of monitoring and control of this type of wastewater treatment plant. One of its special features is the idea of a telecontrol centre which monitors multiple, geographically distributed plants remotely, acts as a centre of expertise, and brings together the expertise of a network of remote experts. Data mining has been identified as a potentially useful contributing technology. Sensor data is now becoming available for some pilot, laboratory scale, and industrial sized digesters.This paper presents the directions of work and emerging results of data mining. Particular themes considered here include:

• •experience gained in the data mining exercise;
• •the use of confidence and prediction intervals;
• •prospects for generalisation over different sizes and types of anaerobic digester;
• •relationship to the overall supervision system developed in the project.

     

How a Pareto frontier complements scenario projections in land use change impact assessment

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m³ ethanol, with GHG emissions of 40 kg CO₂-eq/m³ ethanol. The Pareto frontier indicates an improvement potential of ∼50 $/m³ ethanol when keeping emissions fixed, or ∼250 kg CO₂-eq/m³ ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design.     

Control of an anaerobic digester through normal form of fold bifurcation

Nonlinear dynamics is ubiquitous in engineering systems. As some parameters are varied bifurcations arise in the state variables. Generically, when one parameter changes, Hopf and fold bifurcations are found. Other ones can also be present due to special systems characteristics, such as symmetries. Knowing in advance the significant bifurcation scenario, a novel approach to control can be considered. We compute the normal form corresponding to such a bifurcation and we take this model as the nominal model of the plant. Then we design a nonlinear control which takes advantage of the precise bifurcation scenario. This general method is applied, in this paper, to an anaerobic digester. We will control the process with an adaptive controller.Specifically, we want to compare with the case that the nominal plant is considered as a linear model, such as it is typical in adaptive control techniques. Our proposed method has more benefits in signal control effort, faster convergence rate and low error.This paper shows how the combination of appropriated nonlinear dynamic techniques such as bifurcations and normal forms, and nonlinear control, can give rise to an improvement of the traditional methodology.     

A robust control scheme to improve the stability of anaerobic digestion processes

In this paper, the robust regulation of volatile fatty acids (VFA) and total alkalinity (TA) is addressed to improve the stability of continuous anaerobic digestion (AD) processes. The control scheme is derived from an AD model that includes TA as state variable and it is composed of a model-based multiple-input multiple-output feedback control and an extended Luenberger observer which not only allows the estimation of the process kinetics and variations in the influent composition but also the introduction of an antiwindup structure. The wastewater flow rate is used to regulate the VFA concentration, whereas an alkali solution is added directly to the digester to regulate TA. The controller performance is evaluated via numerical simulations showing excellent responses under the influence of control input saturations, noisy measurements, load disturbances and uncertain kinetics. Finally, it is shown that well-known practical stability criteria for AD processes can be also fulfilled by the proposed control scheme.     

A generalized multistage optimization modeling framework for life cycle assessment-based integrated solid waste management

Solid waste management (SWM) is an integral component of civil infrastructure and the global economy, and is a growing concern due to increases in population, urbanization, and economic development. In 2011, 1.3 billion metric tons of municipal solid waste (MSW) were generated, and this is expected to grow to 2.2 billion metric tons by 2025. In the U.S., MSW systems processed approximately 250 million tons of waste and produced 118 Tg of CO₂e emissions, which represents over 8% of non-energy related greenhouse gas (GHG) emissions, and 2% of total net GHG emissions. While previous research has applied environmental life cycle assessment (LCA) to SWM using formal search techniques, existing models are either not readily generalizable and scalable, or optimize only a single time period and do not consider changes likely to affect SWM over time, such as new policy and technology innovation. This paper presents the first life cycle-based framework to optimize—over multiple time stages—the collection and treatment of all waste materials from curb to final disposal by minimizing cost or environmental impacts while considering user-defined emissions and waste diversion constraints. In addition, the framework is designed to be responsive to future changes in energy and GHG prices. This framework considers the use of existing SWM infrastructure as well as the deployment and utilization of new infrastructure. Several scenarios, considering cost, diversion, and GHG emissions, are analyzed in a 3-stage test system. The results show the utility of the multi-stage framework and the insights that can be gained from using such a framework. The framework was also used to solve a larger SWM system; the results show that the framework solves in reasonable time using typical hardware and readily available mathematical programming solvers. The framework is intended to inform SWM by considering costs, environmental impacts, and policy constraints.     

Assessing water quality management options in the Upper Litani Basin, Lebanon, using an integrated GIS-based decision support system

The widespread and relentless discharge of untreated wastewater into the Upper Litani Basin (ULB) river system in Lebanon has reached staggering levels rendering its water unfit for most uses especially during the drier times of the year. Despite the call by governmental and non-governmental agencies to develop several wastewater treatment plants and sewage networks in an effort to control this problem, these efforts do not seem to be coordinated or based on comprehensive and integrated assessments of current and projected conditions in the basin.This paper provides an overview of the development and implementation of an integrated decision support system (DSS) designed to help policy makers and other stakeholders have a clearer understanding of the key factors and processes involved in the sewage induced degradation of surface water quality in the ULB, and formulate, assess and evaluate alternative management plans. The DSS is developed based on the WEAP model, which provides a GIS based and visual simulation environment and scenario management and analysis capabilities. The DSS was used to assess two main water quality management plans taking into consideration hydrological, spatial and seasonal variabilities. An incremental cost-effectiveness analysis was conducted to identify best buy plans. The results have confirmed the gravity of this problem and demonstrated the importance of taking immediate action on curbing this onslaught on this valuable and scarce fresh water resource.     

基于复合抗扰的溶解氧浓度控制

污水处理中, 出水水质受进水流量、组分以及浓度波动等因素影响. 要保证处理效果, 溶解氧浓度控制应有 较强的鲁棒性. 简单闭环控制的能耗及出水水质波动大. 为此, 本文以污水处理过程1号基准仿真模型模拟污水生 化处理过程, 提出基于扩张状态观测器和滑模控制的复合抗扰控制方法, 以降低对模型信息的依赖, 保证溶解氧浓 度控制效果. 数值仿真结果显示, 复合抗扰控制能够以较小的能耗获得期望的调控效果. 这表明, 复合抗扰控制具有 很好的适应性, 能够满足调控要求, 是一种具有较强实用性的污水处理溶解氧浓度控制方法.     

Mitigation Efforts Calculator (MEC)

The Mitigation Efforts Calculator (MEC) has been developed by the International Institute for Applied Systems Analysis (IIASA) as an online tool to compare greenhouse gas (GHG) mitigation proposals by various countries for the year 2020. In this paper, first we introduce the MEC conceptual model, i.e. the methodology and system architecture. We then discuss the abstract formulation of four different international greenhouse gas trading regimes that are conceivable. Hereafter, the optimization process and its output results, namely cost curves are presented. Finally, we illustrate the MEC as a tool for interactively evaluating complex cost curve information in the context of GHG mitigation targets as currently discussed in international climate policy circles.     

基于PCA-GABP神经网络的BOD软测量方法

针对污水处理过程中关键水质参数无法在线监测的问题．提出基于PCA-GABP神经网络的污水水质软测量方法。该方法由两部分组成：主元分析PCA和GABP神经网络。其中,GABP算法采用局部改进遗传算法优化神经网络权值。并采用自适应学习速率动量梯度下降算法对神经网络进行训练,建立软测量模型。仿真结果表明该软测量模型稳定性好、精度高,可用于污水处理厂对BOD进行在线预测。     

基于干扰观测器的污水处理过程控制研究

溶解氧浓度控制是污水处理过程的核心环节,恰当的溶解氧控制能改进过程性能,而且可以节约能源。但是由于入水水质水量的时变性、非线性和延迟等动态特性,使得基于常规PID的控制效果很不理想,常引起出水水质波动大、能耗高的问题。针对入水水质水量的扰动不确定性设计了基于干扰观测器的PID控制方法,仿真结果表明,此种控制方案鲁棒性强,能有效克服入水干扰的不确定性,具有实际应用前景。     

基于物联网技术的沼气工程运行管理 智能监控系统研究

沼气作为一种可再生的清洁能源,其在农村的应用日益广泛,但其安全性也成为影响推广的一个重要问题。 近年来,对于沼气工程运行管理的监控已经成为沼气行业中一个重要的问题。物联网是一种新兴的信息技术,可以基于传感 技术、传感网络实时获取运行现场的各类数据,在沼气工程运行监控中可以发挥重要的作用。然而,综观近年来的研究和实践 可以发现,用于实际运行的沼气工程运行监控系统仍处于研究的初级阶段。本文从北京市农业局沼气工程运行管理智能监控 的实际需求出发,基于物联网技术设计、实现了沼气工程运行管理智能监控系统,并在北京市进行了具体部署,实际结果表明, 该系统具有良好的应用效果。     

MIMO最小二乘支持向量机污水处理在线软测量研究

污水处理系统是一个包含海量信息的非线性复杂系统。针对污水处理出水水质BOD（生物化学需氧量）、COD（化学需氧量）、TN（总含氮量）等难以在线实时检测等问题,建立了基于在线MIMO-LSSVM（多输入多输出最小二乘支持向量机）和PSO（微粒子群算法）的污水处理软测量模型。仿真结果表明,建立的软测量模型精度高、速度快,能很好地实现污水处理出水指标COD、BOD、TN等参数的实时测量和估计,为污水处理的实时在线控制创造必要的前提条件。     

基于OPC技术的LabVIEW与S7-300 PLC的污水处理监控系统

随着社会经济的迅速发展,污水处理成为环境保护的重要环节,迫切需要对污水处理过程进行先进控制和优化。目前,LabVIEW在工业自动化领域已经有了一些成功应用,但在污水处理系统中却少见报道。本文详细描述了基于LabVIEW的污水处理监控系统的体系结构和软件设计方案,利用SIMATIC.NET建立OPC服务器,为监控提供信息采集接口,并提出了避免通讯阻塞,减少实时数据采集延迟的优化策略。该系统应用于上海达能污水处理厂,可靠、稳定,很好的实现了对污水处理各工艺单元中控制对象的实时监控。     

基于自适应递归模糊神经网络的污水处理控制

针对污水处理过程中具有的非线性、大时变等特征,提出了一种基于自适应递归模糊神经网络(recurrent fuzzy neural network,RFNN)的污水处理控制方法.该方法利用自适应RFNN识别器建立污水处理过程的非线性动态模型,建立的模型可以为RFNN控制器提供污水处理过程中的状态变量信息,保证了控制器根据系统响应调整操作变量的精确性;并且RFNN辨识器及RFNN控制器基于自适应学习率进行学习,确保了递归模糊神经网络的收敛精度和速度,并通过构造李雅普诺夫函数证明了此算法的收敛性;最后,基于基准仿真模型(benchmark simulation model 1,BSM1)平台进行仿真实验.结果表明,与PID、模型预测控制及前馈神经网络相比,该方法对污水处理中溶解氧浓度和硝态氮浓度的跟踪控制精度具有明显的提升.     

Finding an optimum location for biogas plant: a case study for Duzce,Turkey

This study is a case study for modelling and solving a real-life problem. In this study, a practical approximation for finding an optimum location of a foundation was realized with k-means clustering and optimization. Duzce, in the northwest of Turkey, has been researched for the biogas potential to found biogas plant. With this aim, the number of poultry in Duzce has been determined and presented their potential of biogas. Since the number of poultry is quite enough to found a biogas plant, later the location of the poultry farms and their potentials has been determined. Since there are more than 400 poultry farms in Duzce, firstly locations are clustered with classical k-means algorithm. k is specified as 6–8 with an expert knowledge. Later, the nearest location for each cluster center has been attained with simulated annealing with the objective of minimizing the transportation cost. As a result, it has been determined an optimum location for probable biogas plant for Duzce.