Dynamic analysis and split range control for maximization of operating range of continuous microbial fuel cell

Human development is inherently connected with availability of water and energy. Energy production requires water, whereas water treatment needs energy. On the other hand, microbial fuel cell has capability to produce energy and water simultaneously from waste water or organic matter. In this paper, first principle-based model of variable volume microbial fuel cell is simulated. Hydraulic retention time is selected as the manipulated variable using the study of steady state and dynamic responses. Classical PI and model predictive control strategies are developed for controlling the produced power from the cell, and its performance is tested for servo problem. Settling time for positive and negative set points is found to be 126 and 889 h in case of classical PI and 120 and 750 h in case of linear MPC, respectively along with large increase (three times order of magnitude) in working volume for negative set point. These control challenges are overcome by using split range controller with variable and constant volume microbial fuel cells. The settling time for negative set point is found to be 49 and 21 h for classical PI and linear MPC schemes, respectively, which is significantly lower than using only variable volume microbial fuel cell. Also, there is no increase in the working volume of the constant volume microbial fuel cell. Hence, operating range of the microbial fuel cell is enhanced using split range controller.     

Identification and control of a riser‐type FCC unit

This paper addresses the use of feedforward neural networks for the steady‐state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro‐control of the process. It is shown that for a noise‐free system the adaptive neuro‐controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set‐point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi‐loop diagonal PI controller.     

Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and storage

This work presents the implementation of fuzzy logic control (FLC) on a microbial electrolysis cell (MEC). Hydrogen has been touted as a potential alternative source of energy to the depleting fossil fuels. MEC is one of the most extensively studied method of hydrogen production. The utilization of biowaste as its substrate by MEC promotes the waste to energy initiative. The hydrogen production within the MEC system, which involves microbial interaction contributes to the system’s nonlinearity. Taking into account of the high complexity of MEC system, a precise process control system is required to ensure a well-controlled biohydrogen production flow rate and storage application inside a tank. Proportional-derivative-integral (PID) controller has been one of the pioneer control loop mechanism. However, it lacks the capability to adapt properly in the presence of disturbance. An advanced process control mechanism such as the FLC has proven to be a better solution to be implemented on a nonlinear system due to its similarity in human-natured thinking. The performance of the FLC has been evaluated based on its implementation on the MEC system through various control schemes progressively. Similar evaluations include the performance of Proportional-Integral (PI) and PID controller for comparison purposes. The tracking capability of FLC is also accessed against another advanced controller that is the model predictive controller (MPC). One of the key findings in this work is that the FLC resulted in a desirable hydrogen output via MEC over the PI and PID controller in terms of shorter settling time and lesser overshoot.     

Controlling of Proton Exchange Membrane Fuel Cell by Model Predictive Controller Based on ANFIS Model

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro‐fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro‐fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch‐and‐bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.     

Control System Design for a Single Feed ETBE Reactive Distillation Column

Reactive distillation (RD) is advantageous for the Ethyl Tert‐Butyl Ether (ETBE) synthesis. The steady state model of an ETBE reactive distillation column created using the simulator HYSYS is analyzed to synthesize effective control structures. Since the column exhibits input multiplicity with the dual process objectives of ETBE RD (isobutene conversion and ETBE purity), inferential variables are selected. A control structure that organizes a sensitive tray temperature in the stripping section using the reboiler duty and maintains the temperature difference of reactive trays using the reflux flow, is found to be most suitable. A decentralized PI controller and constrained Model Predictive Controller (MPC) are implemented, and performances are compared for set point tracking and disturbance rejection. MPC control algorithms are implemented in MATLAB and interfaced with HYSYS. Constrained MPC (CMPC) is found to be effective for load disturbance rejection, which frequently occurs in the single feed configuration.     

Demand reduction in building energy systems based on economic model predictive control

This paper proposes and demonstrates the effectiveness of an economic model predictive control (MPC) technique in reducing energy and demand costs for building heating, ventilating, and air conditioning (HVAC) systems. A simulated multi-zone commercial building equipped with of variable air volume (VAV) cooling system is built in Energyplus. With the introduced Building Controls Virtual Test Bed (BCVTB) as middleware, real-time data exchange between Energyplus and a Matlab controller is realized by sending and receiving sockets. System identification is performed to obtain zone temperature and power models, which are used in the MPC framework. The economic objective function in MPC accounts for the daily electricity costs, which include time-of-use (TOU) energy charge and demand charge. In each time step, a min–max optimization is formulated and converted into a linear programming problem and solved. In a weekly simulation, a pre-cooling effect during off-peak period and a cooling discharge from the building thermal mass during on-peak period can be observed. Cost savings by MPC are estimated by comparing with the baseline and other open-loop control strategies. The effect of several experimental factors in the MPC configuration is investigated and the best scenario is selected for future practical tests.     

流化床反应器中气相丙烯聚合反应的动力学和预测控制(英文)

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, jus- tifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a central- ized model predictive control （MPC） technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corre- sponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and pro- duction rate while the setpoint tracking of the conventional proportional-integral （PI） controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control vari- ables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceotable for both controlled variables.     

人工湿地-微生物燃料电池系统的发展及展望

人工湿地-微生物燃料电池耦合系统是一种新型生物电化学工艺。在该系统中，人工湿地为微生物燃料电池提供所需的氧化还原梯度和化学能，而微生物燃料电池可以提高人工湿地的处理效能并通过产电的方式回收能源，目前研究主要集中在水处理方面。本文结合近几年人工湿地-微生物燃料电池耦合系统的发展，从系统构建和系统性能的影响因素两个方面综述了人工湿地-微生物燃料电池耦合系统的研究现状，其中影响因素包括系统的组成要素（湿地植物、电极材料、基质材料和微生物）和系统运行参数（有机负荷和废水成分、水力停留时间、溶解氧和进水方式）两个方面。最后提出了人工湿地-微生物燃料电池耦合系统需要解决的主要问题：提高系统的库仑效率，进一步降低构建成本，提高系统去污及产电的综合性能，使该系统最终实现产业化。     

微生物燃料电池阳极产电茵的选育

产电菌能够以微生物燃料电池的阳极作为唯一的电子受体完成有机物的氧化,在产电的同时获得自身生长所需的能量。基于产电菌的特殊代谢方式,有望在处理有机废水的过程中获取电能。分离和培养产电菌是研究其产电过程的基础。本研究采用双层平板技术从厌氧颗粒污泥中分离出数个产电菌株,采用循环伏安法（cyclic voltammograms,CV）对厌氧培养的产电菌进行曲线扫描,所得曲线表明这些产电菌具有一定的电化学活性,可以用来进行产电实验。     

PEM fuel cell current regulation by fuel feed control

We demonstrate that the power output from a PEM fuel cell can be directly regulated by limiting the hydrogen feed to the fuel cell. Regulation is accomplished by varying the internal resistance of the membrane-electrode assembly in a self-draining fuel cell with the effluents connected to water reservoirs. The fuel cell functionally operates as a dead-end design where no gas flows out of the cell and water is permitted to flow in and out of the gas flow channel. The variable water level in the flow channel regulates the internal resistance of the fuel cell. The hydrogen and oxygen (or air) feeds are set directly to stoichiometrically match the current, which then control the water level internal to the fuel cell. Standard PID feedback control of the reactant feeds has been incorporated to speed up the system response to changes in load. With dry feeds of hydrogen and oxygen, 100% hydrogen utilization is achieved with 130% stoichiometric feed on the oxygen. When air was substituted for oxygen, 100% hydrogen utilization was achieved with stoichiometric air feed. Current regulation is limited by the size of the fuel cell (which sets a minimum internal impedance), and the dynamic range of the mass flow controllers. This type of regulation could be beneficial for small fuel cell systems where recycling unreacted hydrogen may be impractical.     

Aeration control of a wastewater treatment plant using hybrid NMPC

In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. As oxygen is consumed by the microorganisms, more oxygen has to be added to the water in order to comply with the required minimum dissolved oxygen concentration. This is done using a set of aerators working on/off that represents most of the plant energy consumption. In this paper a hybrid nonlinear predictive control algorithm is proposed, based on economic and control aims. Specifically, the controller minimizes the energy use while satisfying the time-varying oxygen demand of the plant and considering several operation constraints. A parameterization of the binary control signals in terms of occurrence time of events allows the optimization problem to be re-formulated as an nonlinear programming (NLP) problem at every sampling time. Realistic simulation results considering real perturbations data sets for the inlet variables are presented.     

FPGA based QDMC control for reverse-osmosis water desalination system

Model-based predictive control (MPC) is a widely used advanced control algorithm in process industries, such as chemical plants and oil refineries. Quadratic dynamic matrix control (QDMC) is one of the popular MPC strategies. In this paper, the QDMC controller is used to control a simulated reverse-osmosis (RO) water desalination system with spiral wound element (SWM). A cascaded control system was designed with the QDMC controller and a PID controller for the desalination process, where the QDMC controller optimizes the set point of the PID controller and directly controls one output. In order to make the implementation more convenient, this paper also studies the implementation of QDMC control system based on a field programmable gate array (FPGA) chip. A support software is developed to help engineers adjust the parameters of the QDMC controller. Since the QDMC controller is implemented in an embedded system, the overall system cost is reduced, which is helpful to the application of RO desalination system.     

Subchannel analysis of nanofluids application to VVER-1000 reactor

In this study a thermal-hydraulic analysis is performed of nanofluids as a coolants in subchannels in hot fuel assembly of VVER-1000 reactor by subchannel method. Water-based nanofluids containing various volume fractions of Al₂O₃ nanoparticles are analyzed. The conservation equations and conduction heat transfer equation for fuel and clad have been derived and discretized by finite volume method. The transfer of mass, momentum and energy between adjacent subchannels are split into diversion crossflow and turbulent mixing components. The governing non linear algebraic equations are solved by numerical iteration methods. Finally the nanofluids analysis results are compared with the pure water results. To validate the applied approach, the model and COBRA-EN code results are compared for the case of pure water. Results show that the heat transfer increases with nanoparticles volume concentration in the subchannel geometry.     

鲁棒模型预测控制系统的评估基准

在控制系统的性能评估中,基准的设计是个重要问题。将基本设计极限理论推广到模型预测控制系统(MPC),建立性能评估基准。直接考虑多输入多输出系统的频域扰动,建立输出反馈鲁棒模型预测控制器。此控制器仅仅依赖于过程参数,也是令闭环系统达到控制性能极限的基准控制器。建立了用于评估的性能指标,提出基于此基准的性能评估程序,用以评价其他模型预测控制系统的性能。数学算例证实了这一评估程序的有效性。     

微生物燃料电池及其应用研究进展

简单叙述了微生物燃料电池(MFC)的基本结构及运行原理,从MFC的阳极微生物、阴极结构等方面介绍了MFC的发展现状和研究重点,分析了MFC在替代能源、生物传感器和开发新型水处理工艺等方面的应用前景,指出进一步的研究重点应放在改善电极电化学性能、提高电池输出功率密度和降低电池成本等方面。     

Temperature and Humidity Control of a Micro PEM Fuel Cell Stack

This paper deals with the control of a miniaturised fuel cell system. A single air blower is used to control both heat and water management of the fuel cell. As the number of manipulated variables is smaller than the number of control variables, classical control algorithms are not applicable. To find a suitable controller, a system model is developed that shows the qualitatively same behaviour as the experimental setup. The dynamic behaviour of the model and the influence of the blower are studied by phase portraits. A control algorithm is then conceived by qualitative analysis of the phase portraits and tested in simulations.     

Non-catalyzed cathodic oxygen reduction at graphite granules in microbial fuel cells

Oxygen is the most sustainable electron acceptor currently available for microbial fuel cell (MFC) cathodes. However, its high overpotential for reduction to water limits the current that can be produced. Several materials and catalysts have previously been investigated in order to facilitate oxygen reduction at the cathode surface. This study shows that significant stable currents can be delivered by using a non-catalyzed cathode made of granular graphite. Power outputs up to 21 W m⁻³ (cathode total volume) or 50 W m⁻³ (cathode liquid volume) were attained in a continuous MFC fed with acetate. These values are higher than those obtained in several other studies using catalyzed graphite in various forms. The presence of nanoscale pores on granular graphite provides a high surface area for oxygen reduction. The current generated with this cathode can sustain an anodic volume specific COD removal rate of 1.46 kg_COD m⁻³ d⁻¹, which is higher than that of a conventional aerobic process. This study demonstrates that microbial fuel cells can be operated efficiently using high surface graphite as cathode material. This implies that research on microbial fuel cell cathodes should not only focus on catalysts, but also on high surface area materials.     

Characteristics‐Based Model Predictive Control of a Catalytic Flow Reversal Reactor

This paper describes the formulation and tuning of a model‐based controller for a catalytic flow reversal reactor (CFRR). A plug flow non‐linear pseudo‐homogeneous mathematical representation of the process is used to model the mass and energy transport phenomena for the model‐based controller. A combination of the method of characteristics and model predictive control (MPC) technology is used to formulate the controller (Shang et al., Ind. Eng. Chem. Res. 43 (9) 2140–2149 (2004)). Mass extraction from the midsection of the reactor is used as the manipulated variable. Numerical simulations are used to show the performance of the formulated controller. The performance of the controller is evaluated on a simulated catalytic flow reversal reactor unit for combustion of lean methane streams for reduction of greenhouse gases emissions.     

串级控制对过程预测控制的影响分析与对策

化工过程控制中普遍设置以流量控制为副回路的串级控制来实现对温度、液位和成分等被控变量的控制。预测控制的操作变量在很多情况下也是流量,其控制作用的实现要靠底层的流量控制回路。本文针对由于现场串级控制结构不允许改变,流量副回路只能接收温度等主控制器的输出作为其给定值,造成上层预测控制的操作变量无法直接下载到流量控制回路的问题,分别提出了一种将上层优化输出通过一阶惯性滤波作用于主回路控制器和一种将串级控制中流量对主被控变量的传递函数嵌入预测控制模型的实施方案,通过Shell标准重油分馏塔的控制问题进行仿真实验证明了两种方案的可行性,并对其控制性能进行了比较分析。两种方法理论上构思简单,实际中易于实现,具有普遍适用性。