Investigation on thermal radiation spectra of coal ash deposits

This paper deals with thermal radiation properties of ash deposits on a pulverized coal boiler of an electric power plant. Normal emittance spectra in the 2.5–25 μm interval, and total normal emittance, were measured on 4 kinds of ash layers of a mm magnitude order thickness, at 560 → 1460 → 560 K in heating and cooling. It was found that ash powder layers are opaque for infrared radiation. The emittance increases with ash radiation wavelength and temperature. Ash powder is sintered and fused above 1200 K. The emittance of the sintered layer is above that of the unsintered layer. The authors propose, and explain by an example, correlating the experimentally obtained emittance spectra of ash deposits with a continuous curve, the formula of which defines the dependence of emittance on wavelength and temperature, i.e. ε = ε(λ,Т). Use of this formula, with parameter values determined by the proposed methodology, may greatly simplify the practical application of the experimentally determined emittances in the thermal design of existing and new steam boiler furnaces.     

Feedforward-feedback control of a solid oxide fuel cell power system

One of the main challenges for wide-spread utilization of the solid oxide fuel cell (SOFC) power systems is how to achieve high electrical efficiency without increasing the degradation rate of the fuel cells. To run the SOFC power system at high efficiency over a long period of time, properly designed controllers are indispensable.Although a number of various approaches to control SOFC have been proposed so far, it seems that the design of control system, along with simple tuning procedure, has not been treated in a consistent manner. This issue is addressed in the present paper resulting in a feedforward-feedback control structure. The feedforward part is based on the stoichiometry of electro-oxidation, reforming and combustion reactions, which allow immediate response to variable current demand. The feedback part performs additional fine adjustment of fuel and air supply in order to minimize the undesired system temperatures variations. The selection of pairings of manipulated and controlled variables for control is based on physical knowledge of the system. Input/output pairing for single-loop feedback control is assessed by the relative gain analysis. An efficient procedure for tuning the parameters of the feedback controllers is suggested, relying on simple open-loop step responses of the system.The proposed low-level control is assessed on a detailed physical model of a 2.5 kW SOFC power system by simulating two nonstationary load regimes. Simulations show that the control provides a robust operation under large load variations while meeting the operating constraints. Due to its simplicity, the control is feasible for implementation on a real SOFC system.     

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.     

The linear assignment method for multicriteria group decision making based on interval‐valued Pythagorean fuzzy Bonferroni mean

The interval‐valued Pythagorean fuzzy sets can easily handle uncertain information more flexibly in the process of decision making. Considering the interrelationship among the input arguments, we extend the Bonferroni mean and the geometric Bonferroni mean to the interval‐valued Pythagorean fuzzy environment and solve its practical application problems. First, we develop the interval‐valued Pythagorean fuzzy Bonferroni mean and the weighted interval‐valued Pythagorean fuzzy Bonferroni mean (WIVPFBM) operators. The properties of these aggregation operators are investigated. Then, we also develop the interval‐valued Pythagorean fuzzy geometric Bonferroni mean and the weighted interval‐valued Pythagorean fuzzy geometric Bonferroni mean (WIVPFGBM) operators and analyze their properties. Third, we utilize the WIVPFBM and WIVPFGBM operators to fuse the information in the interval‐valued Pythagorean fuzzy multicriteria group decision making (IVPFMCGDM) problem, which can obtain much more information in the process of group decision making. With the aid of the linear assignment method, we present its extension and further design a new algorithm for the application of IVPFMCGDM. Finally, an example is given to elaborate our proposed algorithm and validate its excellent performance.     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.