厌氧消化过程的非模型控制

对废水淤泥的厌氧消化处理过程进行了研究．提出了一种基于神经网络的非模型控制方法。多变量控制系统的操作变量为供热量和进水量．被控变量为消化温度和消化污泥排出浓度。证明了控制算法的收敛性。讨论了控制系统的稳定性。仿真结果证实了非模型控制方法的有效性。该方法无需对象的模型．为复杂生化过程的控制提供了一种新途径。     

农业面源污染管理政策调控仿真模型研究进展

农业面源污染因其分散性、滞后性和不确定性而成为环境治理的难点。面源污染物迁移转化过程的模拟模型发展迅速,而农业面源污染管理政策仿真评估模型研究相对较为滞后。系统梳理与分析了自上而下宏观目标约束、自下而上微观行为传导及宏观与微观上下耦合三类政策仿真模型研究进展,总结了当前模型的基础理论与方法的优势与不足。本文提出了融合流域水系统过程规律认知,构建宏观政策目标自上而下约束与微观主体行为自下而上传导耦合的农业面源污染政策仿真模型,实现政策仿真模拟在国家、区域、流域与栅格跨尺度上的嵌套传导、参数互验与系统预测,以解决因农业面源污染防治主体的多元性与防治对象的广泛性特征所导致的复杂性系统难题,使得政策效应模拟仿真结果空间显性化,进而实现农业面源污染控制的政策管理由粗放型向精准化方向迈进。     

Improvement of nitrogen removal and reduction of operating costs in an activated sludge process with feedforward–cascade control strategy

In this paper, a feedforward–cascade controller for dissolved oxygen concentration in an activated sludge process is designed in order to meet stricter effluent quality standards at a minimum cost. Conventional proportional and integral (PI) constant dissolved oxygen set-point control and feedforward–cascade dissolved oxygen set-point control are evaluated using the reduced model of activated sludge model no. 1 and reduced IWA simulation benchmark. The feedforward–cascade control has been based on a hierarchical structure where a high level or cascade control selects the set-point of the low level or conventional controller and low level directly control dissolved oxygen concentration. And feedforward control is introduced in the control system for preventing the influent loading from influencing the system. Simulation results show that feedforward–cascade control of the activated sludge process is more successful than conventional PI control in meeting the effluent standards and reducing operational costs. This control strategy can be expected to be accepted by the operating personnel in wastewater treatment plants.     

On-line load monitoring of wastewaters with a respirographic microbial sensor

This paper demonstrates the functionality, laboratory testing and field application of a microbial sensor that is capable of monitoring the organic pollution extent of wastewaters both off-line in a laboratory and on-line in a wastewater treatment plant. The biosensor was first developed in the laboratory using synthetic wastewater and then applied to monitor the effluent of the unit. The basic working principle of the biosensor is based on the on-line measurement of CO2 concentration in the off gas produced during carbon compound degradation by microbial respiration activities. CO2 concentration under operation conditions (constant oxygen flow rate, residence time and pH) is closely related to the extent of organic pollution (biochemical oxygen demand, chemical oxygen demand). CO2 monitoring is carried out by an infrared spectrometer, whereas current organic pollution is determined off-line according to the conventional 5-day lasting BOD analysis. Off gas analysis of CO2 concentration strongly correlates with off-line biochemical oxygen demand measurements allowing continuous on-line monitoring of the organic load within a wastewater treatment plant. Thus, real time process control and operation become feasible.     

Safety Risk Assessment and Improvement in a Food Production Process

The food industry is under pressure to improve food safety, implement efficient risk management, and control quality “from farm to fork.” During processing of raw materials into packaged consumable products, food may be influenced by biological and chemical pollution and other various environmental factors. Risk assessment of a food production process was studied in this work. The novelty of this work lies in analyzing the development laws of food safety risk in serial and parallel modes, and developing a heuristic algorithm of polynomial time to minimize safety risks in food production with a constant budget. This algorithm was validated by a numerical example of peanut milk production. The primary procedures in peanut milk production were found and the advice on investment allocation was given to improve production.     

用藻类监测和评价图们江的水污染

在图们江上、中游进行了人工基质着生藻类调查和室内栅藻检测,以监测和评价水污染状况。在所采用的野外监测指标中,以着生藻类总数与污染程度的关系最为明显。为便于比较和综合,采用了相对藻类总数(PN) 根据调查结果,提出了可作为图们江污染的指示种类。多样性指数与污染的关系,因受到其他非污染因子的影响,并不稳定。研究结果表明:图们江的主要污染源是尾矿废水(悬浮物)和纸浆废水(悬浮物、色度和有毒物质)。最严重污染江段为菜队到图们江段,次之为南坪江段。       

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.     

Disturbance-accommodating control approach applied for the control of a continuous stirred tank reactor

This paper describes an experimental study of linear adaptive control to achieve the monitoring of a continuous stirred tank reactor. The practical control objective was the regulation of the substrate concentration at a pre-specified value in the process effluent despite local changes and/or culture physiology variations. The substrate concentration and the dilution rate have been selected as the controlled and the control variable respectively. The results obtained confirm that this approach offers the possibility to combine simplicity and effectiveness in bioprocess control.     

Control of anaerobic digesters using Generic Model Control

This paper describes the improved effluent control of industrial anaerobic digestion treatment systems. Previous research has shown the possibility of applying a model-based adaptive control technique to these processes. This paper reviews this technique and shows that improved controllability can be obtained by a modification to the existing algorithm, based on a new control technique known as Generic Model Control, that simplifies and improves the robustness of the control algorithm.     

Neural network prediction of thermophilic (65 degrees C) sulfidogenic fluidized-bed reactor performance for the treatment of metal-containing wastewater

The performance of a fluidized-bed reactor (FBR) based sulfate reducing bioprocess was predicted using artificial neural network (ANN). The FBR was operated at high (65 degrees C) temperature and it was fed with iron (40-90 mg/L) and sulfate (1,000-1,500 mg/L) containing acidic (pH = 3.5-6) synthetic wastewater. Ethanol was supplemented as carbon and electron source for sulfate reducing bacteria (SRB). The wastewater pH of 4.3-4.4 was neutralized by the alkalinity produced in acetate oxidation and the average effluent pH was 7.8 +/- 0.8. The oxidation of acetate is the rate-limiting step in the sulfidogenic ethanol oxidation by thermophilic SRB, which resulted in acetate accumulation. Sulfate reduction and acetate oxidation rates showed variation depending on the operational conditions with the maximum rates of 1 g/L/d (0.2 g/g volatile solids (VS)/d) and 0.3 g/L/d (0.06 g/g VS/d), respectively. This study presents an ANN model predicting the performance of the reactor and determining the optimal architecture of this model; such as best back-propagation (BP) algorithm and neuron numbers. The Levenberg-Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20. The developed ANN model predicted acetate (R=0.91), sulfate (R=0.95), sulfide (R=0.97), and alkalinity (R=0.94) in the FBR effluent. Hence, the ANN based model can be used to predict the FBR performance, to control the operational conditions for improved process performance.     

The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm

This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.     

Machine learning-based model predictive controller design for cell culture processes

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments.     

Computational modeling of evolutionary learning processes in the brain

The evolutionary selection circuits model of learning has been specified algorithmically. The basic structural components of the selection circuits model are enzymatic neurons, that is, neurons whose firing behavior is controlled by membrane-bound macromolecules called excitases. Learning involves changes in the excitase contents of neurons through a process of variation and selection. In this paper we report on the behavior of a basic version of the learning algorithm which has been developed through extensive interactive experiments with the model. This algorithm is effective in that it enables single neurons or networks of neurons to learn simple pattern classification tasks in a number of time steps which appears experimentally to be a linear function of problem size, as measured by the number of patterns of presynaptic input. The experimental behavior of the algorithm establishes that evolutionary mechanisms of learning are competent to serve as major mechanisms of neuronal adaptation. As an example, we show how the evolutionary learning algorithm can contribute to adaptive motor control processes in which the learning system develops the ability to reach a target in the presence of randomly imposed disturbances.     

Biosorption of Copper by a Bacterial Biofilm on a Flexible Polyvinyl Chloride Conduit

Inexpensive technologies with less-than-optimal efficiencies as a strategy for countering economic restraints to pollution control have been evaluated by using a laboratory-scale biotreatment process for copper-containing effluent. Economizing measures include the use of polyvinyl chloride (PVC) cylinders fashioned from commercially available flexible PVC conduit to support a biofilm that was cultured in an inexpensive medium prepared in wastewater. The biofilm was challenged by aqueous copper solution in a bioreactor and subsequently analyzed under a scanning electron microscope with energy-dispersive X-ray microanalysis.     

Biosorption of copper by a bacterial biofilm on a flexible polyvinyl chloride conduit

Inexpensive technologies with less-than-optimal efficiencies as a strategy for countering economic restraints to pollution control have been evaluated by using a laboratory-scale biotreatment process for copper-containing effluent. Economizing measures include the use of polyvinyl chloride (PVC) cylinders fashioned from commercially available flexible PVC conduit to support a biofilm that was cultured in an inexpensive medium prepared in wastewater. The biofilm was challenged by aqueous copper solution in a bioreactor and subsequently analyzed under a scanning electron microscope with energy-dispersive X-ray microanalysis.     

Optimal feeding strategies by adaptive mesh selection for fed-batch bioprocesses

The objective of this contribution is the design of optimal feeding strategies for fed-batch bioprocesses, where complex dynamic models with input and state constraints are present. For the solution of this dynamic optimization problem a transformation to a finite dimensional optimization problem is made using piecewise linear control profiles. The optimization of these profiles is performed by a sequential approach, that includes an ODE solver for the solution of the model ODE's. Further an adaptive mesh selection algorithm was investigated for an appropriate discretization of the control profiles. The implementation of the resulting optimal feeding profiles is shown for a process example, namely the production of nikkomycin by Streptomyces tendae. This implementation uses a hierarchical process control framework, that consists of components for process monitoring, state estimation, and trajectory control.     

Anthropogenic impact on fish parasite fauna in lakes

Anthropogenic influence on the fish parasite fauna in lakes is studied. Three types of the influence are considered, namely pollution by industrial effluent, anthropogenic eutrophication, and development of aquaculture. Their effects on the fish parasite fauna were found to be different.     

Feedforward-feedback control of dissolved oxygen concentration in a predenitrification system

As the largest single energy-consuming component in most biological wastewater treatment systems, aeration control is of great interest from the point of view of saving energy and improving wastewater treatment plant efficiency. In this paper, three different strategies, including conventional constant dissolved oxygen (DO) set-point control, cascade DO set-point control, and feedforward-feedback DO set-point control were evaluated using the denitrification layout of the IWA simulation benchmark. Simulation studies showed that the feedforward-feedback DO set-point control strategy was better than the other control strategies at meeting the effluent standards and reducing operational costs. The control strategy works primarily by feedforward control based on an ammonium sensor located at the head of the aerobic process. It has an important advantage over effluent measurements in that there is no (or only a very short) time delay for information; feedforward control was combined with slow feedback control to compensate for model approximations. The feedforward-feedback DO control was implemented in a lab-scale wastewater treatment plant for a period of 60 days. Compared to operation with constant DO concentration, the required airflow could be reduced by up to 8–15% by employing the feedforward-feedback DO-control strategy, and the effluent ammonia concentration could be reduced by up to 15–25%. This control strategy can be expected to be accepted by the operating personnel in wastewater treatment plants.