Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map

Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.     

Experimental validation of a resilient monitoring and control system

Complex, high performance, engineering systems have to be closely monitored and controlled to ensure safe operation and protect public from potential hazards. One of the main challenges in designing monitoring and control algorithms for these systems is that sensors and actuators may be malfunctioning due to malicious or natural causes. To address this challenge, this paper addresses a resilient monitoring and control (ReMAC) system by expanding previously developed resilient condition assessment monitoring systems and Kalman filter-based diagnostic methods and integrating them with a supervisory controller developed here. While the monitoring and diagnostic algorithms assess plant cyber and physical health conditions, the supervisory controller selects, from a set of candidates, the best controller based on the current plant health assessments. To experimentally demonstrate its enhanced performance, the developed ReMAC system is then used for monitoring and control of a chemical reactor with a water cooling system in a hardware-in-the-loop setting, where the reactor is computer simulated and the water cooling system is implemented by a machine condition monitoring testbed at Idaho National Laboratory. Results show that the ReMAC system is able to make correct plant health assessments despite sensor malfunctioning due to cyber attacks and make decisions that achieve best control actions despite possible actuator malfunctioning. Monitoring challenges caused by mismatches between assumed system component models and actual measurements are also identified for future work.     

基于MCGS的饮用水水质安全监测系统的软件设计

饮用水水质安全越来越引起人们的广泛关注。本文针对饮用水水质监测的需求,设计出一种基于MCGS组态的水质在线监测软件。监测软件利用MCGS组态工具设计实现,界面与设备之间的通信通过组态软件的串口和MCGS脚本驱动程序共同实现。文中介绍了系统的结构、人机交互界面和脚本驱动的软件设计流程和方法,并通过测试表明该软件能准确监测饮用水的多项参数,且稳定可靠、操作方便,达到软件设计要求。     

基于PLC和组态王的农村水厂监控系统设计

运用PLC和组态软件技术,设计了农村饮水工程水厂供水自动化监控系统。详细阐述了系统总体方案、硬件配置、控制策略、软件流程和监控界面。实际应用效果表明,本系统安全可靠,操作控制方便。     

Cluster analysis by self-organizing maps: An application to the modelling of water quality in a treatment process

The unit processes in water treatment involve many complex physical and chemical phenomena which are difficult to assess using traditional data analysis methods. Moreover, measurement data gathered from the process is often challenging with respect to modelling purposes, because there is a lack of continuous online measurements, for which sparse laboratory measurement data have to be conducted to compensate them.This paper reports on the application of self-organizing map (SOM) techniques combined with K-means clustering to model water quality in the treatment of drinking water. At the first phase of the study, a SOM was produced by using both on-line and laboratory data of the treatment process and raw water. At the second phase, the reference vectors of the map were classified by K-means algorithm into clusters, which can be used to present different states of the process. At the final phase, the results were interpreted by analyzing the reference vectors in the clusters. The introduced approach offers a straightforward method for assessing the essential characteristics of the process. In addition, the results clearly demonstrate some challenges in the modelling of water quality in treatment processes.     

Microfluidic applications of functionalized magnetic particles for environmental analysis: focus on waterborne pathogen detection

The continuous surveillance of drinking water is extremely important to provide early warning of contamination and to ensure continuous supplies of healthy drinking water. Isolation and detection of a particular type of pathogen present at low concentration in a large volume of water, concentrating the analyte in a small detection volume, and removing detection inhibiting factors from the concentrated sample, present the three most important challenges for water quality monitoring laboratories. Combining advanced biological detection methods (e.g., nucleic acid-based or immunology-based protocols) with microfluidics and immunomagnetic separation techniques that exploit functionalized magnetic particles has tremendous potential for realization of an integrated system for pathogen detection, in particular, of waterborne pathogens. Taking advantage of the unique properties of magnetic particles, faster, more sensitive, and more economical diagnostic assays can be developed that can assist in the battle against microbial pathogenesis. In this review, we highlight current technologies and methods used for realization of magnetic particle-based microfluidic integrated waterborne pathogen isolation and detection systems, which have the potential to comply in future with regulatory water quality monitoring requirements.     

Assessing pollution risks to water supply intakes using geographical information systems (GIS)

The process of pollution risk assessment requires the assimilation of data that are spatially variable in nature, making geographical information systems (GIS) an ideal tool for such assessments. Over half of Britain's drinking water is obtained from surface water abstractions, many of which are situated in upland areas. In order to optimise the quality of abstracted waters it is important to assess the possible risks of pollution upstream from the point of abstraction. This paper describes the use of the PC-based WINGS™ and MapInfo Professional™ geographical information systems in the evolution of a risk assessment methodology to assess catchment risk. The work illustrates how such technology can assist in environmental decision-making to optimise the quality of drinking water supplies and enhance treatment efficiencies. Examples are given showing how raster and vector-based data can be used within a GIS framework to produce maps indicating areas of potential hazard to water quality, and coupled with existing models to predict and quantify risk frequency and impact. GIS techniques are further utilised in the formulation of a raw water monitoring programme to assist in intake operation and land-use planning in the catchment. The availability of suitable digital data was found to be variable, and some problems encountered in their integration and implementation within the system framework were resolved. Comment is given on the suitability and relative performance of the two software packages in the assessment of catchment risk. The work was carried out on a medium specification desktop PC, and therefore has the potential to be utilised across the intranet of a large utility company.     

城市生活污水处理自控系统设计

本文以某城市污水处理厂自动控制系统的方案选择与设计过程为例，说明了基于PLC的自动控制方案是当今城市污水处理厂的最佳选择。作者详细论述了该污水处理自动控制系统的方案规划、硬件和软件配置、工作原理和基于罗克韦尔RSView32组态软件的上位监控系统以及该污水处理厂生产过程自动化系统与管理信息系统的有机结合。本实例为大型污水处理厂自动化系统的设计和实施提供了颇有价值的借鉴。     

供水工程自动化控制系统设计

针对V型滤池净水处理工艺,设计了以冗余PLC为现场控制站、工控机为监控管理机的全厂计算机监控系统.本控制系统将工控机、PLC、变频器、传感器和执行机构有机地结合起来,发挥各自优势,软件设计合理,系统调试较方便;且系统各级之间可独立运行,保证了水厂的不间断生产.     

Good modelling practice in drinking water treatment,applied to Weesperkarspel plant of Waternet

Good modelling practice increases the credibility and impact of the information and insight that modelling aims to generate. It is known to be crucial for model acceptance and it is a necessity to amass a long-term, systematic thorough knowledge base for both science and decision making. This paper shows how ten steps in model development and evaluation can also be applied to numerical modelling of drinking water treatment, using models of drinking water treatment processes of the Weesperkarspel treatment plant of Waternet. The Weesperkarspel plant consists of ozonation, pellet softening, biological activated carbon filtration and slow sand filtration. For the different processes models were developed that were used for operational improvements. The modelling resulted in new insights and knowledge about the treatment processes and improved operation of the processes. From scenario studies for the pellet softening it was concluded that chemical dosing can be diminished when by-pass ratio is increased and that pellet size can be controlled by measuring the difference in pressure guaranteeing fluidisation of the pellet bed. In addition, ozone dosage can be optimised by modelling ozone exposure, bromate formation and biologically degradable natural organic matter (NOM) under varying influent water quality.     

InTouch组态软件在火电厂化学水处理系统中的应用

介绍了美国Wonderware公司的InTouch工控组态软件.结合火电厂化学水处理监控系统工程实例,分析了系统的控制方案及结构,并对于监控系统的设计及功能开发进行了详细的阐述.     

Modeling and dynamic optimization of fuel-grade ethanol fermentation using fed-batch process

This paper investigates optimization of operational strategies of an industrial ethanol fermentation process. One of the challenges associated with this type or process is that most of the measurements are only taken sporadically, thereby complicating process monitoring and optimization. The one exception to this rule involves temperature measurements, which are readily available. However, an existing model of the plant investigated in this paper does not include an energy balance and, accordingly, the temperature measurements cannot be used to estimate model parameters. This paper addresses these deficiencies and proposes modifications to an existing ethanol fermentation model. The proposed changes include the derivation of an energy balance, modification of the reaction kinetics to include additional inhibition terms, and also estimation of model parameters from industrial data. The new model is validated against plant data and then used for optimization of the process operations. It is shown that modifications of the input profiles for the cooling rate and the glucoamylase addition can lead to an approximately 10% increase in ethanol yield. These are promising results, even though these findings will ultimately need to be validated during real plant operations.     

智能自控调度系统在马庄供水中的应用研究

本项目运用工业控制组态软件，实现对农村供水工程中蓄水池水位远程控制、水处理的自动化监测、机泵运行自动化控制及运行参数的自动化监测、视频监视等项智能技术，基本形成了农村饮水安全工程智能化信息管理系统建设，满足农村饮水安全工程自动化和信息化的需要。     

邳州市农村饮用水安全信息化建设方案研究

农村饮水安全保障是脱贫攻坚目标的重要内容之一,农村饮用水安全工程是一项惠及广大群众的民生工程。为加强农村饮用水安全工程运行管理,有效保障民生用水安全,邳州市水务局开展农村饮用水安全信息化建设,在充分研究当地农村饮用水现状基础上,提出邳州市农村饮用水安全信息化建设方案,采用一体化设计思路,分区分层确保用水信息安全,开发泵站监控、水量水质监测、管网运行监视、水费计收等业务应用功能,建立科学高效的水安全管理信息系统,提高管理水平,达到农村饮用水安全工程水量自动控制、水质有效保障、水费精确收取等目标要求,为保障农村居民饮用水安全提供决策支持。     

Data validation and missing data reconstruction using self-organizing map for water treatment

Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as optimization of coagulation dosing must therefore account for possible sensor failures and imperfect input data. In this paper, self-organizing map (SOM)-based methods are applied to multiparameter data validation and missing data reconstruction in a drinking water treatment. The SOM is a special kind of artificial neural networks that can be used for analysis and visualization of large high-dimensional data sets. It performs both in a nonlinear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. Combining the SOM results with those obtained by a fuzzy technique that uses marginal adequacy concept to identify the functional states (normal or abnormal), the SOM performances of validation and reconstruction process are tested successfully on the experimental data stemming from a coagulation process involved in drinking water treatment.     

物联网背景下的城市污水可视化监控体系研究

为了解决目前人类饮用水普遍遭到污染的问题,保护水资源,防治水污染,文中以常州为例,在物联网背景下,提出了一种基于虚拟仪器技术的城市污水可视化监控体系的设计方法,同时给出了系统的整体方案和软硬件结构。该系统可以对人类饮用水的污染源进行远程全面监控,并对水质进行监测。     

Simple Metrics for Improving Software Process Performance and Capability: A Case Study

This paper presents a case study which describes the application of statistical process performance monitoring and capability assessment on a large process control software project. The process performance and capability monitoring used in the case study is part of a Quality Framework for Software Development (QFSD) devised for the development of control systems at Fisher–Rosemount Systems and provides a practical alternative to ISO9000 quality models. The process performance measurements were based on commonly available metrics that could be obtained with the minimum disruption to the processes being examined. The application of statistical methods were used to establish problem areas at the earliest opportunity allowing process adjustments to be made to improve the process performance. The processes used were defined in terms of work products which were categorized and evaluated for the level of completion. This allowed a process capability to be calculated. Those processes which were found to be at a lower capability level became the focus for the process improvement for the next project. In particular the capability measurement identified processes where questions needed to be raised about the relevance of the process, whether the techniques used were effective and whether the tools were adequate. It was found that achieving high performance and capability is a learning process with the development team improving with each new project. The benefits have been better managed, more cost and time effective projects producing higher quality software. Management and the development team have obtained a better understanding of the software development process and this continuing learning process has lead to a continuous improvement in both the development methodology and the resulting software. The paper presents examples of the real benefits that can be obtained by tracking process performance and assessing process capability at all stages, and shows that this can be achieved without the need to resort to complex procedures for process measurement, as most of the empirical data came from commonly available process data.     

An SPC monitoring system for cycle-based waveform signals using haar transform

Due to the rapid development of computer and sensing technology, many measurements of process variables are readily available in manufacturing processes. These measurements carry a large amount of information about process conditions. It is highly desirable to develop a process monitoring and diagnosis methodology that can utilize this information. In this paper, a statistical process control monitoring system is developed for a class of commonly available process measurements-cycle-based waveform signals. This system integrates the statistical process control technology and the Haar wavelet transform. With it, one can not only detect a process change, but also identify the location and estimate the magnitude of the process mean shift within the signal. A case study involving a stamping process demonstrates the effectiveness of the proposed methodology on the monitoring of the profile-type data. Note to Practitioners-Cycle-based signal refers to an analog or digital signal that is obtained through automatic sensing during each operation cycle of a manufacturing process. The cycle-based signal is very common in various manufacturing processes (e.g., forming force in stamping processes, the holding force, and the current signals in spot welding processes, the insertion force in the engine assembly process). In general, cycle-based signals contain rich process information. In this paper, cycle-based signal monitoring will be accomplished by monitoring the wavelet transformation of the signal, instead of monitoring the raw observations themselves. Further, a decision-making technique is developed using the SPC monitoring system to locate where the mean shift occurred and to estimate magnitudes of mean shifts. Thus, this paper presents a generic framework for the enhanced statistical process control technique of cycle-based signals.     

Chlorcast©: a methodology for developing decision-making tools for chlorine disinfection control

Managers of drinking water supply systems are increasingly preoccupied with changes that may occur in the quality of water from the time it leaves the treatment plant until it reaches the consumer's tap. Modeling the quality of water in a distribution system and more particularly the evolution of residual chlorine may constitute an interesting means for more efficient management. This article presents the Chlorcast© methodology which provides the guidelines on building a decision-making tool, based on the use of artificial neural networks, for chlorination control in the final disinfecting phase. Two examples will demonstrate the ability of this method to represent the dynamics of the evolution of residual chlorine by making it possible to forecast residual chlorine in the City of Sainte-Foy's drinking water tank and distribution system.     

Gear-box fault detection using time-frequency based methods

Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected. Two different time–frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen–Loeve basis. Both of them detect the gear-box fault with an acceptable detection delay of maximum 100s, which is neglectable compared with the fault developing time.