LFT modelling and identification of anaerobic digestion

In this paper, a reduced order model of anaerobic digestion is first proposed, with the main goal to develop an efficient tool for process monitoring and control. Then, in order to perform parameter estimation, the model has been rewritten in a linear fractional transformation (LFT) formulation, using a symbolic tool originally developed for linear models and modified for the processing of nonlinear models. Two different test cases have been considered. In a first case, the data used for parameter identification have been generated by simulating the well known and more complex ADM1 model, considering waste activated sludge as substrate. In a second case, experimental data were collected on a laboratory scale equipment, operated in a semi-batch experiment, performing the anaerobic digestion of ultra-filtered cheese-whey.     

鸡冠石污水处理厂污泥厌氧消化处理运行实践

简要介绍了重庆市鸡冠石污水处理厂的污泥处理与沼气系统的工艺流程,着重论述了污泥厌氧消化工艺的调试与运行,对运行记录进行了分析,得出了该厂污泥厌氧消化系统已成功运行的结论。     

Biogas recovery from microwave heated sludge by anaerobic digestion

Biogas generated from sewage sludge,livestock waste,and food waste by anaerobic digestion is a valuable renewable energy resource.However,conventional anaerobic digestion is not an efficient process.A long hydraulic retention time and low biogas recovery rate hinder the applications of those resources.An effective pretreatment method to destroy sludge microbial cells has been one of the major concerns regarding improvement of the biogas production.This article focuses on the effects of microwave heating on ...     

On-line diagnosis and uncertainty management using evidence theory––experimental illustration to anaerobic digestion processes

The on-line diagnosis is a key requirement in biological processes. This is particularly true in the case of wastewater treatment processes due to the composition of media, the requirements of operating conditions and the wide variety of possible disturbances that necessitate careful and constant monitoring of the processes. Moreover, because only partial information is available in an on-line context and because of the technical and biological complexities of the involved processes, specific characteristics are required for diagnosis purposes. Several approaches like quantitative model based, qualitative model based and process history based methods were applied over the years. This paper present a methodological framework based on evidence theory to manage the fault signals generated by conventional approaches (i.e., residuals from hardware and software redundancies, fuzzy logic based modules for process state assessment) and to account for uncertainty. The advantages of using evidence theory like modularity, detection of conflict and doubt in the information sources are illustrated with experimental results from a 1 m³ fixed bed anaerobic digestion process used for the treatment of industrial distillery wastewater.     

A new adaptive PI for bicarbonate regulation in an anaerobic digestion process

This paper deals with the design and validation via numerical simulations of an adaptive proportional integral (PI) controller for bicarbonate regulation in an anaerobic wastewater treatment process. The proportional and integral gains are deduced as a function of the parameters of an approximate dynamical model. First, a brief explanation of the control structure is introduced. After that, a general equation of an adaptive PI controller is proposed. Next, the controller is implemented in order to regulate bicarbonate by two control actions in a completely stirred tank reactor (CSTR): adding a base and manipulating the dilution rate. Finally, a smooth switching mechanism which allows the process to operate in open or closed loop according to the operating conditions is implemented. The performance of the process with the proposed control scheme is validated via simulations. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于遗传算法优化的变论域模糊控制在厌氧消化过程的应用

面对具有较强非线性、不确定性和难以建立精确数学模型的控制对象,传统PID控制很难达到理想的控制效果,而模糊控制却是一种非常有效的智能控制方法,但是常规模糊控制精度低,超调量大,同时模糊控制规则的设计存在较强的主观性,难以把握。本文采用遗传算法优化改进的变论域模糊控制中的伸缩因子,当误差较大时,将控制作用的伸缩因子乘以一个较大系数,增加控制作用,提高系统输出,迅速减小误差;当误差较小时(本文取误差小于±0.025),将伸缩因子在原有的基础上乘一个较小的系数,减少控制作用范围,使系统输出趋于平稳,从而使控制规则分布更加合理,减少对专家知识的依赖。实验结果表明,同传统PID控制策略、常规模糊控制策略和常规变论域模糊控制策略相比,无论在动态性能方面,还是在稳态性能方面,改进的变论域模糊控制策略的控制效果均优于其他3种控制策略。     

Reverse logistics network design for a biogas plant: An approach based on MILP optimization and Analytical Hierarchical Process (AHP)

Among sustainable energy production processes, methanation (anaerobic co-digestion) has a high potential to valorize organic residual waste by exploiting its energetic capacities in the form of biogas. Nevertheless, at the early stage of the project, decisions must be made concerning the network used to supply biomass to the anaerobic co-digestion facility. However, these decisions involve complex hierarchical processes, taking into account the best compromise to be found among diverse factors and actors (economic, social, environmental, etc.). In this article a systematic approach integrating Mixed Integer Linear Programming (MILP) optimization and Analytical Hierarchical Process is proposed. It will allow project managers to evaluate possible scenarios for the implementation of an anaerobic co-digestion logistics network in order to facilitate the integration of the preferences of the stakeholders involved in the project. The approach proposed is then illustrated by the design of a municipal biogas facility in Nancy, France.     

Design and study of a risk management criterion for an unstable anaerobic wastewater treatment process

In this paper, we consider an unstable biological process used for wastewater treatment. This anaerobic digestion ecosystem can have two locally stable steady states and one unstable steady state. We first study the model and characterise the attraction basin associated to the normal operating mode. In the second step, we estimate the size of this attraction basin by using a simplified criterion that turns out to be a good approximation. Finally, we apply the approach on a real anaerobic digestion plant, and we show that the proposed criterion allows to rapidly detect the conditions of a destabilisation.     

Black-, gray-, and white-box modeling of biogas production rate from a real-scale anaerobic sludge digestion system in a biological and advanced biological treatment plant

Neural Computing and Applications - Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling...     

A discrete‐time nonlinear state observer for the anaerobic digestion process

This paper is concerned with the design of an LMI‐based discrete‐time nonlinear state observer for an anaerobic digestion model. In presence of disturbances in both the dynamics of the model and the output measurement signals, the proposed observer robustly estimates all state variables including bacteria concentrations, which are costly and difficult to measure. In the goal to increase applicability of the proposed observer for other systems, we present the theoretical results in a general way. First, due to the use of Young's inequality in a convenient way, we get new sufficient conditions, expressed in terms of bilinear matrix inequalities (BMIs), ensuring the criterion. Then, to render the BMIs convex, two alternative solutions are proposed, where both lead to linear matrix inequality (LMI) conditions. It is shown analytically and numerically that these two solutions provide less conservative LMI conditions compared to the existing methods in the literature. To validate the proposed methodology on a real‐world model, an application to an anaerobic digestion model is given.     

Modeling and optimization of activated sludge bioreactors for wastewater treatment taking into account spatial inhomogeneities

In this work, we study optimal and suboptimal control strategies for the treatment of a polluted water resource by using aside a continuous bioreactor. The control consists in choosing the inlet volumetric flow rate for filling the bioreactor with contaminated water from a considered resource (lake, reservoir, water-table, …). The treated outflow returns to the resource. We tackle an optimization problem which aims to minimize the time needed to reach a prescribed minimal value of contamination in the resource by choosing the input flow. Next, we study the influence of inhomogeneities of concentrations in the bioreactor, considering a system based on partial differential equations which describe its dynamics. We show that applying the optimal feedback control derived for perfectly mixed bioreactor does not allow to reach the target with small diffusion parameters as it drives the bioreactor to washout (the bioreactor equilibrium with no biomass). In this case, a suboptimal feedback (which reaches the target in finite time) is obtained with the help of a Hybrid Genetic Algorithm. Furthermore, we consider that the fluid flow velocity of the water entering into the bioreactor follows either a uniform or a nonuniform profile, showing that the optimal volumetric flow rates obtained with the uniform profile are not optimal if the profile is nonuniform, even when high diffusion coefficients are considered in the model.     

Simultaneous COD and VFA unmeasured process inputs estimation in actual anaerobic wastewater treatment processes

A Virtually Controlled Observer (VCO) was proposed to estimate both, the unmeasured states and the unknown inputs (influent substrate concentrations: Chemical Oxygen Demand (DCO) and Volatile Fatty Acids (VFA) in a 1 m³ up-flow fixed-bed anaerobic digestion (AD) pilot plant. The VCO is developed without the full knowledge of the AD kinetics and consists of an asymptotic observer and an output feedback controller, in which, one of the observer inputs (the hypothetical -unmeasured- influent substrate concentration) is updated by a feedback control law, which is devised to regulate the estimation error of a measured output. Results show that the proposed observer is robust in the face model mismatch, load and operating conditions changes and sensor faults as it was able to reconstruct the influent substrate concentration.     

线性离散随机系统输入和状态的多步估计方法及应用

具有未知输入的系统的状态估计问题已经在过去几十年里引起了相当的关注.本文对于线性离散随机系统提出了一种基于多步信息的输入和状态同步估计方法.首先,采用多步信息的最小方差方法来获得未知输入.由于引入了包含多个时间步骤的扩张状态和测量向量而计算多步信息,使估计结果与一步估计相比减少了对噪声的敏感性.其次,利用输入估计值和卡尔曼滤波估计过去和当前的状态.该方法在未知输入维数等于状态维数时仍然有良好的估计效果.数值仿真验证了提出的估计方法的有效性.最后,该方法应用于厌氧消化过程反应罐中的溶解甲烷和二氧化碳的浓度估计以验证方法的实用性.     

Maximizing the set of recurrent states of an MDP subject to convex constraints

This paper focuses on the design of time-homogeneous fully observed Markov decision processes (MDPs), with finite state and action spaces. The main objective is to obtain policies that generate the maximal set of recurrent states, subject to convex constraints on the set of invariant probability mass functions. We propose a design method that relies on a finitely parametrized convex program inspired on principles of entropy maximization. A numerical example is provided to illustrate these ideas.     

Factors affecting anaerobic power output in the Margaria-Kalamen test

The purpose of this study was to determine the effect of three approach distances and two vertical ascent methods on approach velocity, vertical velocity and power output of the Margaria-Kalamen test. Male (n = 43) and female (n = 53) university students were tested using a traditional and a modified (ramp) Margaria-Kalamen test with approaches of 2, 6 and 10m. The average of Ave trials for each approach distance was used to calculate anaerobic power. Males averaged 14.0% greater approach velocity, 28.8% greater vertical velocity and 41.1% greater power output than females, regardless of approach distance or vertical ascent method. Anaerobic power output was not significantly different between the sexes for any method when the effects of the percentage of fat and lean body mass were removed by the covariance technique. The 2m approach produced significantly slower approach velocity, vertical velocity and power output than the other approaches. The ramp method resulted in significantly greater approach velocity (11.5%), vertical velocity (9.6%) and anaerobic power output (9.4%) than the stair method. Males averaged 8.4% higher power output using the ramp, while females averaged 11.0% higher than the conventional stair method. Females appeared to benefit more than males from using a ramp in the anaerobic power test. Anaerobic power measurement from the Margaria-Kalamen method can be maximized using a ramp method.     

Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller.     

Fuzzy control structure for an anaerobic fluidised bed

This article deals with the design of a fuzzy control strategy for a fluidised bed reactor, which is used for anaerobic wastewater treatment. This strategy is composed of a supervisor system and two PI L/A controllers. In addition, a biomass observer, designed on the basis of the Takagi–Sugeno approach considering a principal component analysis, is used with supervision proposals. The supervisor is also designed following the Takagi–Sugeno methodology; it detects the process state, selects and applies the most adequate control action in order to avoid the washout region. On the other side, two control actions are designed for bicarbonate regulation using the PI/LA technique: adding a base and dilution rate. These control actions, as well as the open loop operation, are selected by the supervisor in order to reject disturbances on the substrate influent allowing at the same time a high methane production. The applicability of the proposed structure in a fluidised bed reactor is illustrated via simulations.     

Optimal cycle production of a manufacturing system subject to deterioration

A single part type, single machine, fluid model manufacturing system is considered. The machine is subject to a deterministic deterioration process, which depends on the operation rate. The objective is to minimize a long term average cost index which penalizes inventory surplus and backlog. The optimal policy determined presents some similarities with the policy conjectured optimal in the past for a similar Markovian formulation. The considered problem can be applied to several real contexts, and not just from the manufacturing domain, as briefly discussed in the paper.     

Increasing the dilution rate can globally stabilize two-step biological systems

We revisit two-step mass-balance models of biological processes as met to describe numerous biological systems including the anaerobic digestion or the nitrification process in view of its global stabilization. We show that when a bi-stability occurs, it can be possible to globally stabilize the dynamics toward an unique positive equilibrium by increasing the dilution rate. We give sufficient conditions on the growth functions of the model for this situation to appear. This illustrates that for biological multi-step reactional systems, increasing the residence time (e.g. decreasing the input flow rate) may not be the only way to stabilize the dynamics.     

A continuous-time integrated model for discrete control of production flows in a multi-level bills of material environment

An integrated dynamic model for discrete production scheduling and continuous capacity expansion is presented in this paper. The modeled manufacturing system, based on multi-level bills of materials, is characterized by flexible machines with negligible setups and production rates fixed for the current capacity. Make-to-stock formulation of the problem is studied and optimal behavior of the system is determined with the help of the maximum principle. A fast time-decomposition algorithm is suggested to locate the optimal solution.