Demos T. Tsahalis

Operation optimization of an industrial cogeneration system by a genetic algorithm

Large process plants need energy in several forms (mechanical energy, electricity, steam, hot water etc.), which very often come from a variety of sources such as gas-turbine generators, steam-turbine generators, exhaust gas boilers, fuel-burning boilers etc. In addition, the utility network serves as a source of supplementary electricity if needed, or as a sink when excess electricity is produced. The cost of energy is one of the major contributors to the total operating cost of a process plant. Consequently, minimization of this cost is of utmost importance. Due to the variety of energy sources, the interdependency between sources and the variation of technical and economic conditions with time (e.g. change of load, deterioration of equipment, change of fuel and electricity prices etc.), the task of minimizing energy cost is far from trivial. Methods and algorithms to solve these types of problems are still a subject of research because of the following reasons: the problems are usually nonlinear with multimodal objective functions that may contain both discrete (e.g. integer) and continuous variables. No single method has been successful with every problem of this type. In the present work, a genetic algorithm (GA) is applied for the operation optimization of a cogeneration system, which supplies a process plant with electricity and steam at various pressure levels. A mathematical simulation model of the system has been developed, taking into consideration the real condition of main equipment, as it is revealed by an appropriate set of measurements. The GA is combined with the simulation model, in order to solve the optimization problem under specified constraints.

A Genetic Algorithm for operation optimization of an industrial cogeneration system

Genetic Algorithms (GAs) have been developed in the last three decades in an attempt to imitate the mechanics of the selection process in natural genetics. They also contain many elements of expert systems. In the present work, a GA is applied for the optimization of the operation of a cogeneration system, which supplies a process plant with electricity and steam at various pressure levels. A mathematical simulation model of the system has been developed taking into consideration the real conditions of the main equipment, as determined by an appropriate set of measurements. The GA is combined with the simulation model in order to solve the optimization problem under specified constraints. The capability of GAs to handle objective functions of any complexity with both discrete (e.g., integer) and continuous variables, as well as their capability of optimizing only on the basis of the results of the simulation model, make GAs successful in this type of problems.

A genetic algorithm for optimal positioning of actuators in active noise control: results from the ASANCA project

In order to achieve maximum noise reduction inside an aircraft cabin through the use of an active noise control system (ANCS), it is important that the number and positions of the sensors for monitoring the noise field; the control system for driving the actuators; and the number and positions of the actuators that generate the secondary noise field, which partially cancels the primary noise field, must be optimally determined. An optimization strategy for the positioning of the actuators, based on genetic algorithms (GA), is presented, assuming a fixed sensor configuration and a given control system. The application of the developed GA to a propeller aircraft is also discussed. The work presented was performed under the CEC BRITE/EURAM‐Aeronautics project “ASANCA”, in which a demonstrator ANCS was developed.

Development of an Expert System Shell Based on Genetic Algorithms for the Selection of the Energy Best Available Technologies and their Optimal Operating Conditions for the Process Industry

The development of genetic algorithms started almost three decades ago in an attempt to imitate the mechanics of natural systems. Since their inception, they have been applied successfully as optimization methods, and as expert systems, in many diverse applications. In this paper, a genetic-algorithm-based expert system shell is presented that, when combined with a proper database comprising the available energy-saving technologies for the process industry, is able to perform the following tasks: (a) identify the best available technologies (BATs) among the available ones for a given process industry, and (b) calculate their optimal design parameters in such a way that they comply with the energy requirements of the process. By the term BAT is meant the available energy-saving technology, among the existing ones in the market, that is the best for the case.

Vibration/noise optimization of a FEM railway wheel model

This paper presents the optimization of the design of a railway wheel in terms of the wheels sound power levels emission, with respect to its geometrical properties. To this end, a simplified finite element method (FEM) model of the wheel was employed, that did not include the interaction of the wheel and rail or the influence of the braking system that is assembled on the wheel. The objective of the optimization method was to find a design of the selected railway wheel, which without the use of damping or tuning devices, emits less vibration/noise compared to the original design. The optimization method used, was based on genetic algorithms (GAs). GAs are a robust optimization method that performs regardless of the optimization problem. The GA‐based optimization method that is presented in this paper, utilized ANSYS running in batch mode for the calculation of the objective function values of the population of each generation.

Adaptive Pulse Width/Phase Modulated Controller for a High Frequency Active Electro-Hydraulic Pump System

In this article, an adaptive width/phase differential modulated controller is designed for an active electro-hydraulic pump (AehP) system consisting of a single straight cylinder. This controller adjusts the duty cycle and relative phase between the incoming and outgoing valve. The adjustment relies on the maximization of a cost function that characterizes the net outflow rate. The developed model relies on computational fluid dynamics (CFD). Transient CFD analysis shows that the pressure propagation causes the pump performance to be highly dependent to the overall dimensions of the hydraulic system, the fluid used and the operating conditions, thus constraining and complicating the controller design process. The controllers adaptation mechanism relies on the cyclic coordinate method, which adjusts each parameter in a periodic manner. Simulation studies are used to investigate the efficiency of the proposed controller with respect to the pump-chambers length, the pistons pulsating frequency, and the fluid properties

Development of a MATLAB code based on a weak formulation of wave propagation through a porous material: comparison with experimental results

In the present paper a weighted residual formulation of wave propagation through a porous material for a three‐dimensional case, based on the theoretical formulation of Zwikker and Kosten for sound propagation in porous material, is described. Based on this formulation, a MATLAB code was developed which simulates an experimental configuration that consists of: a double wall cavity, formed by two parallel aluminum panels; and a reverberation room. A loudspeaker is placed on the reverberation room to provide the acoustical excitation of the double wall set‐up. The results which are obtained from the MATLAB code, in terms of the sound pressure level (SPL) in the double wall cavity and the displacement of the two panels, are compared with the corresponding experimental ones for the cases of air and thermal insulation material being the medium filling the double wall cavity.

A Genetic Algorithm Method for Optimal Position of the Actuator Configuration of an Active Vibration Control System for Trim Panels in Aircrafts

Abstract Active Noise Control (ANC) became in the last decade a very popular technique for controlling low-frequency noise. The increase in its popularity is a consequence of the rapid development in the field of computers in genera1 and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. In this paper, the successful application of a Genetic Algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented.