Improved process control of an industrial sludge centrifuge-dryer installation through binary logistic regression modeling of the fouling issues

Biological wastewater treatment generates huge amounts of waste sludge which need to be dewatered and eventually dried to minimize transportation and incineration costs. A characteristic feature of sludge in this context is that it turns into a sticky substance during its drying process inducing fouling problems in the drying installation. At the wastewater treatment plant of Monsanto in Antwerp, Belgium, one enclosed centrifuge-dryer system is used to dry the sludge. In the past, this installation had to be shut down regularly due to dryer fouling problems. To avoid these operational problems, a binary logistic regression analysis is presented in this research based on a 5-year database, resulting in an empirical model for the evaluation of the dryer fouling risk as a function of the sludge feed characteristics. The model inputs are the sludge volume index (SVI) and the dosing of clay additive and tertiary (flotation) sludge, the latter containing polyaluminumchloride (PACl), to the sludge feed of this particular system.By exploiting the knowledge captured by this model, the derived control strategy is based on the value of the SVI. Whenever the SVI is high the original high clay dosing to the feed needs to be maintained. At moderate SVI values, implying an intrinsically better sludge dewaterability, the strategy dictates a reduction in the clay dosing to the sludge feed to have a reduced sludge solids dryness after dewatering, thereby avoiding that the sludge exhibits its most sticky phase when passing the most fouling sensitive part of the dryer. When the SVI is lower than 50 mL/g the control strategy states that conditioning of the sludge with PACl is required to mask the stickiness instead of postponing it, avoiding that the stickiness of the sludge already hampers the dewatering stage of the process.     

Knowledge representation in an activated sludge plant diagnosis system

This paper focuses on the knowledge representation framework utilized by an integrated wastewater treatment expert system for diagnosing operational problems of an activated sludge plant. The system deals with events that may occur in all the units of an activated sludge plant and exploits on-line measurements, observations formed from data provided by laboratory analyses and empirical observations in an integrated manner. The system provides assistance to human experts to control the activated sludge process. It has been tested and evaluated in the pilot activated sludge plant of the Water and Air Quality Laboratory in the University of the Aegean.     

Bilinear black-box identification and MPC of the activated sludge process

In this paper the activated sludge process, which is a process for biological nitrogen removal in municipal wastewater treatment plants, is modeled as a discrete-time bilinear system by application of a recursive prediction error method system identification technique. A novel bilinear model predictive control algorithm is also derived and applied on a simulation model of the activated sludge process. For discrete-time bilinear systems, a quadratic cost on the predicted outputs and inputs, together with input/state constraints, results in a nonlinear non-convex optimization problem. An investigation is performed where the suggested control algorithm is compared with a linear counterpart. The results reveals that even though the identified bilinear black-box model describes the dynamics of the activated sludge process better than linear black-box models, bilinear model predictive control only gives moderate improvements of the control performance compared to linear model predictive control laws.     

Predictive control of an activated sludge process: An application to the Viikinmäki wastewater treatment plant

In this work, we discuss the application of multivariable predictive control for the activated sludge process in a full-scale municipal wastewater treatment plant. Emphasis is given to the selection of a control configuration that contributes to minimising the economic costs while improving the removal efficiency of the nitrogen compounds. For this task, a simple dynamic matrix control algorithm is favoured for controlling the nitrogen concentrations at the end of the biological process. The behaviour of the activated sludge process is reproduced in a commercial simulator that acts as a real-time testing platform and that is also used for identifying the multivariable input–output models for the predictive controller. For demonstrating the effectiveness of the proposed approach, different control configurations are considered and compared against the aeration control strategies currently used at the plant. Based on the simulation results, this work shows the potentiality of the dynamic matrix control which is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent.     

Risks to Effective Knowledge Sharing in Agile Software Teams: A Model for Assessing and Mitigating Risks

We present an empirically grounded and theoretically informed model for the assessment and mitigation of risks to effective knowledge sharing in agile development. The model is anchored in empirical insights from four agile projects across two software companies and in extant research on risk‐strategy analysis and knowledge sharing in software development. We develop the model as part of the long‐standing tradition of presenting risk management models dedicated to specific issues in software development and confirm its practical usefulness in one of the software companies studied. The model offers concepts and processes to assess a project's knowledge sharing risk profile and articulate an overall resolution strategy plan to mitigate the risks. The results highlight how different knowledge sharing risk management profiles can lead to different project performance outcomes. We conclude with a discussion of research opportunities that the results offer software development scholarship. © 2016 John Wiley & Sons Ltd     

Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression

The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking.     

利用三维细胞自动机模拟活性污泥法的处理过程

针对活性污泥法的污水处理过程,提出了利用三维细胞自动机模型进行模拟的方法．该方法通过对曝 气池内活性污泥法处理过程动态演化过程的模拟,直观地揭示出活性污泥法的污水处理过程．用实际数据进行了验 证,证明了该方法能够有效地展现曝气池内污水净化的过程,实现了其可视化,并描绘了活性污泥的生长曲线．     

污水处理净化过程的三维细胞自动机动态模拟

针对污水处理过程的复杂性、不可重复性和不可再现性等特点,根据活性污泥的净化机理与动力学特性,在Mckinney等人建立的经典活性污泥法动力学模型的基础上,提出了一种模拟活性污泥净化过程的三维格子气细胞自动机模型.该模型的演化规则根据微生物的增殖规律和经典的动力学模型设计,模型通过模拟有机物和微生物的扩散、反应和沉降过程,反应了活性污泥的整个净化过程.对模型进行的仿真实验结果表明:该模型不仅可以复现污水净化过程,而且更直观地刻画了活性污泥法污水处理过程的动态演化行为,直接反映出活性污泥法系统的表观特征.     

活性污泥法污水处理数学模型的发展和应用

综述了活性污泥法污水处理数学模型自20世纪50年代以来的发展历程； 阐述了国际水协会（IWA）推出的活性污泥1号、2号、2D号、3号模型（ASM1、ASM2、ASM2D、ASM3）各自的特点和使用限制条件； 介绍了几种基于ASM系列模型的具有代表性的商业化仿真软件． 最后，还就ASM系列模型的三个应用难点——水质的分析测定、模型简化、参数校正进行了讨论．     

城市污水处理过程污泥膨胀识别与抑制综述

污泥膨胀是活性污泥法污水处理过程中常见的一类异常工况, 且具有严重危害性, 研究污泥膨胀的识别和抑制方法对城市污水处理过程正常运行意义重大. 本文主要针对城市污水处理过程中污泥膨胀的识别和抑制方法进行综述. 首先, 文章概述了城市污水处理过程, 介绍了污泥膨胀的概念、主要特点、类型和成因; 其次, 概述了基于微生物生理特征、机理模型、图像识别和数据驱动的污泥膨胀识别方法, 分析其发展现状并指出优缺点; 然后, 概述了基于过程调控和机理特征的污泥膨胀抑制方法, 分析其发展现状并对比优缺点; 最后, 总结全文, 指出了城市污水处理过程污泥膨胀识别和抑制面临的主要问题, 并对其研究趋势进行了展望.     

Generic distributed parameter model control of a biological nutrient removal (BNR) activated sludge process

This paper addresses nonlinear control of a class of distributed parameter systems by the generic distributed parameter model-based control (GDPMC) strategy. Case studies on a complex biological nutrient removal (BNR) activated sludge process for nitrogen and phosphorus removal show that the GDPMC strategy is applicable for controlling species of interest in specific bioreaction zones. The designs give much improved performances compared to well-tuned PI controllers in terms of the integral time absolute errors (ITAE). In a study on control of the entire BNR activated sludge plant, a multi-unit decentralised GDPMC strategy is shown to be robust to 25% plant-model mismatch in three sensitive and uncertain kinetic parameters. The advantages of the GDPMC strategy over lumped-model generic model control (GMC) are demonstrated in a comparative study on soluble phosphate control within the anaerobic zone of the plant.     

Procedures for parameter and state estimation of microbial growth process models

Practical problems concerning parameter and state estimation of microbial growth processes, with application to batch and continuous fermentation processes and to the activated sludge wastewater treatment process are discussed. Besides the usual aggregated single substrate-single organism mass balance model with Michaelis-Menten growth dynamics, an alternative model, in which the biomass concentration is divided into age classes, is introduced. A method in which the parameters were adjusted manually with sensitivity functions was used for parameter estimation. With the aid of some examples it was shown that the Michaelis-Menten model is not practically identifiable. A recursive state-estimation algorithm for control and supervision purposes was developed and its application in combination with both the aggregated and the age distribution model was demonstrated.     

污水处理过程中对泥龄的模糊神经网络控制

针对污水处理过程的高度非线性、进水流量及水质变化剧烈、各状态变量之间存在强耦合关系等特性，提出了一种自适应模糊神经网络控制方法，以泥龄作为运转控制参数，调节排出的污泥量．仿真结果表明该控制器能够在线调整输入变量的隶属函数、优化控制规则，将其应用于活性污泥法污水处理系统中，可以快速地去除污水中的污染物，使污泥具有良好的去污能力和沉淀性能，并且具有很强的鲁棒性．     

An advanced integrated expert system for wastewater treatment plants control

The activated sludge process is a commonly used method for treating wastewater. Due to the biological nature of the process it is characterized by poorly understood basic biological behavior mechanisms, a lack of reliable on-line instrumentation, and by control goals that are not always clearly stated. It is generally recognized that an Expert System (ES) can cope with many of the common problems relative with the operation and control of the activated sludge process. In this work an integrated and distributed ES is developed which supervises the control system of the whole treatment plant. The system has the capability to learn from the correct or wrong solutions given to previous cases. The structure of the suggested ES is analyzed and the supervision of the local controllers is described. In this way, the main problems of conventional control strategies and individual knowledge-based systems are overcome.     

A nonlinear observer for activated sludge wastewater treatment process: Invariant observer

An activated sludge wastewater treatment process model is concerned in this paper. In order to estimate the variables that cannot be measured online, an invariant observer for activated sludge wastewater treatment process is presented. The invariant observer can measure biomass concentration, substrate concentration and dissolved oxygen concentration in high accuracy and rapidity. Meanwhile it can be structured by means of typical form, and its robust convergence property is verified by theoretical analysis and numerical simulations (MATLAB ).     

A recursively identified model for short-term predictions of NH4/NO3 – concentrations in alternating activated sludge processes

One of the stumbling blocks in the operation of alternatingly aerated activated sludge processes (ASPs) for nitrogen removal is the limited knowledge of both the varying influent composition and the complex dynamics of the biological process. This paper presents a simple physical N-removal model for alternatingly aerated, continuously mixed ASPs. The simplicity is achieved by capturing the slower process dynamics in recursively estimated time-varying model parameters. Both seasonal and diurnal parameter variations are tracked. Also the influent ammonium concentration is treated as a recursively estimated model parameter. The method performs excellently on real data collected from an alternatingly aerated pilot scale ASP fed with municipal wastewater. Simulation of the resulting time-varying model yields accurate and computationally cheap predictions of ammonium and nitrate concentrations in the specific plant under operation over the next hours. Simulation for different control input scenarios can be used to optimize process performance, either manually by operators or automatically by model based optimizing controllers. Another possible application is optimization of the sludge (biomass) concentration, as the estimated parameters contain information regarding process load and concentrations and activities of the N-removing biomass. From this information it can be computed whether there is an excess/shortage of sludge in the reactor.     

活性污泥法污水处理过程的建模与仿真技术的研究

综述了活性污泥法污水处理过程的建模及仿真技术的发展 .在分析活性污泥法污水处理过程现状的基础上 ,阐述了传统数学模型、智能模型以及混合模型的建模方法 ,并介绍了目前活性污泥系统仿真技术的发展现状 .     

Robust multivariable control of complex biological processes

This paper addresses advanced control of a biological nutrient removal (BNR) activated sludge process. Based on a previously validated distributed parameter model of the BNR activated sludge process, we present robust multivariable controller designs for the process, involving loop shaping of plant model, robust stability and performance analyses. Results from three design case studies showed that a multivariable controller with stability margins of 0.163, 0.492 and 1.062 measured by the normalised coprime factor, multiplicative and additive uncertainties respectively give the best results for meeting performance robustness specifications. The controller robustly stabilises effluent nutrients in the presence of uncertainties with the behaviour of phosphorus accumulating organisms as well as to effectively attenuate major disturbances introduced as step changes. This study also shows that performance of the multivariable robust controller is superior to multi-loops SISO PI controllers for regulating the BNR activated sludge process in terms of robust stability and performance and controlling the process using inlet feed flowrate is infeasible.     

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics.     

Fuzzy Dempster–Shafer reasoning for rule-based classifiers

In real classification problems intrinsically vague information often coexist with conditions of “lack of specificity” originating from evidence not strong enough to induce knowledge, but only degrees of belief or credibility regarding class assignments. The problem has been addressed here by proposing a fuzzy Dempster–Shafer model (FDS) for multisource classification purposes. The salient aspect of the work is the definition of an empirical learning strategy for the automatic generation of fuzzy Dempster–Shafer classification rules from a set of exemplified training data. Dempster–Shafer measures of uncertainty are semantically related to conditions of ambiguity among the data and then automatically set during the learning process. Partial reduced beliefs in class assignments are then induced and explicitly represented when generating classification rules. The fuzzy deductive apparatus has been modified and extended to integrate the Dempster–Shafer propagation of evidence. The strategy has been applied to a standard classification problem in order to develop a sensitivity analysis in an easily controlled domain. A second experimental test has been conducted in the field of natural risk assessment, where vagueness and lack of specificity conditions are prevalent. These empirical tests show that classification benefits from the combination of the fuzzy and Dempster–Shafer models especially when conditions of lack of specifity among data are prevalent. ©1999 John Wiley & Sons, Inc.