The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.
In this study the authors minimise the total process cost for the heating of solid particles in a horizontal fluidised bed by an optimal choice of the inlet heating gas temperature profile and the total gas flow. Solid particles flowed along the apparatus and were heated by a hot gas entering from the bottom of the fluidised apparatus. The hydrodynamics of the fluidised bed is described by a two-phase Kunii - Levenspiel model. We assumed that the gas was flowing only vertically, whereas solid particles were flowing horizontally and because of dispersion they could be additionally mixed up in the same direction. The mixing rate was described by the axial dispersion coefficient. As any economic values of variables describing analysing process are subject to local and time fluctuations, the accepted objective function describes the total cost of the process expressed in exergy units. The continuous optimisation algorithm of the Maximum Principle was used for calculations. A mathematical model of the process, including boundary conditions in a form convenient for optimisation, was derived and presented. The optimization results are presented as an optimal profile of inlet gas temperature. The influence of heat transfer kinetics and dispersion coefficients on optimal runs of the heating process is discussed. Results of this discussion constitute a novelty in comparison to information presented in current literature.
This article contains information concerning of the analysis the possibility of defining refinery qualities of the slag based thermophysical and thermodynamical data. The paper presents a model of slag refining processes and a method of determining the reduction capability of slag solutions. Slag was analysed with the use of the DTA methods for the brass melting conductions. The study of computer program including the satisfactory number of data there are used in to the design a modern device rotating head used for gas-slag refining. It was achieved that the refining gas and fluxes were distributed ever by the rotating head. High effectiveness of the gas-slag refining processes was proved for the brass.
The predicted annual growth of energy consumption in ICT by 4% towards 2020, despite improvements and efficiency gains in technology, is challenging our ability to claim that ICT is providing overall gains in energy efficiency and Carbon Imprint as computers and networks are increasingly used in all sectors of activity. Thus we must find means to limit this increase and preserve quality of service (QoS) in computer systems and networks. Since the energy consumed in ICT is related to system load, ]this paper discusses the choice of system load that offers the best trade-off between energy consumption and QoS. We use both simple queueing models and measurements to develop and illustrate the results. A discussion is also provided regarding future research directions.
Evolutionary computing and algorithms are well known tools of optimisation that are utilized for various areas of analogue electronic circuits design and diagnosis. This paper presents the possibility of using two evolutionary algorithms - genetic algorithm and evolutionary strategies - for the purpose of analogue circuits yield and cost optimisation. Terms: technologic and parametric yield are defined. Procedures of parametric yield optimisation, such as a design centring, a design tolerancing, a design centring with tolerancing, are introduced. Basics of genetic algorithm and evolutionary strategies are presented, differences between these two algorithms are highlighted, certain aspects of implementation are discussed. Effectiveness of both algorithms in parametric yield optimisation has been tested on several examples and results have been presented. A share of evolutionary algorithms computation cost in a total optimisation cost is analyzed.
An on-line optimising control strategy involving a two level extended Kalman filter (EKF) for dynamic model identification and a functional conjugate gradient method for determining optimal operating condition is proposed and applied to a biochemical reactor. The optimiser incorporates the identified model and determines the optimal operating condition while maximising the process performance. This strategy is computationally advantageous as it involves separate estimation of states and process parameters in reduced dimensions. In addition to assisting on-line dynamic optimisation, the estimated time varying uncertain process parameter information can also be useful for continuous monitoring of the process. This strategy ensures that the biochemical reactor is operated at the optimal operation while taking care of the disturbances that are encountered during operation. The simulation results demonstrate the usefulness of the two level EKF assisted dynamic optimizer for on-line optimising control of uncertain nonlinear biochemical systems.
The demand for a net reduction of carbon dioxide and restrictions on energy efficiency make thermal conversion of biomass a very attractive alternative for energy production. However, sulphur dioxide emissions are of major environmental concern and may lead to an increased corrosion rate of boilers in the absence of sulfatation reactions. Therefore, the objective of the present study is to evaluate the kinetics of formation of sulphur dioxide during switchgrass combustion. Experimental data that records the combustion process and the emission formation versus time, carried out by the National Renewable Energy Institute in Colorado (US), was used to evaluate the kinetic data. The combustion of switchgrass is described sufficiently accurate by the Discrete Particle Method (DPM). It predicts all major processes such as heating-up, pyrolysis, combustion of switchgrass by solving the differential conservation equations for mass and energy. The formation reactions of sulphur dioxide are approximated by an Arrhenius-like expression including a pre-exponential factor and an activation energy. Thus, the results predicted by the Discrete Particle Method were compared to measurements and the kinetic parameters were subsequently corrected by the least square method until the deviation between measurements and predictions was minimised. The determined kinetic data yielded good agreement between experimental data and predictions.
Production processes at KGHM are complex and require from customers products of constantly higher quality at relatively lowest prices. Such situation results in an increase of the importance of optimisation of processes. As products and technologies change rapidly, technologists at the plant in Głogów have less time to achieve optimisation basing on own experiences. Analysing a particular process, we can e.g. detect occurring disturbances, find factors having an influence on quality problems, select optimal settings or compare various production procedures. Analysis of the course of production process is the basis of process optimisation. One optimisation in case of the process of decopperisation of flash slag can be a change of a technological additive to a less energy-consuming one, and its final result can be an improvement of the productivity index, a change of the relation between final effects and born expenditures, as well as optimisation of production costs.
Micro perforated panel (MPP) absorber is a new form of acoustic absorbing material in comparison with porous ones. These absorbers are considered as next generation ones and the best alternative for traditional porous materials like foams. MPP combined with a uniform air gap constructs an absorber which has high absorption but in a narrow bandwidth of frequency. This characteristic makes MPPAs insufficient for practical purposes in comparison with porous materials. In this study instead of using a uniform air gap behind the MPP, the cavity is divided into several partitions with different depth arrangement which have parallel faces. This method improves the absorption bandwidth to reach the looked for goal. To achieve theoretical absorption of this absorber, equivalent electro-acoustic circuit and Maa’s theory (Maa, 1998) are employed. Maa suggested formulas to calculate MPP’s impedance which show good match with experimental results carried out in previous studies. Electro-acoustic analogy is used to combine MPP’s impedance with acoustic impedances of complex partitioned cavity. To verify the theoretical analyses, constructed samples are experimentally tested via impedance tube. To establish the test, a multi-depth setup facing a MPP is inserted into impedance tube and the absorption coefficient is examined in the 63–1600 Hz frequency range. Theoretical results show good agreement compared to measured data, by which a conclusion can be made that partitioning the cavity behind MPP into different depths will improve absorption bandwidth and the electro-acoustic analogy is an appropriate theoretical method for absorption enhancement research, although an optimisation process is needed to achieve best results to prove the capability of this absorber. The optimisation process provides maximum possible absorption in a desired frequency range for a specified cavity configuration by giving the proper cavity depths. In this article numerical optimisation has been done to find cavity depths for a unique MPP.
The following paper presents an idea of minimising the number of connections of individual piezoelectric transducers in a row-column multielement passive matrix system used for imaging of biological media structure by means of ultrasonic projection. It allows to achieve significant directivity with acceptable input impedance decrease. This concept was verified by designing a model of a passive ultrasonic matrix consisting of 16 elementary piezoceramic transducers, with electrode attachments optimised by means of electronic switches in rows and columns. Distributions of acoustic field generated by the constructed matrix model in water and results of the calculations conformed well.
The following work presents the idea of constructing a digitally controlled active piezoceramic transducer matrix for ultrasonic projection imaging of biological media in a similar way as in case of roentgenography (RTG). Multielement ultrasonic probes in the form of flat matrices of elementary piezoceramic transducers require attaching a large number of electrodes in order to activate the individual transducers. This paper presents the idea of minimising the number of transducer connections in an active row-column matrix system. This idea was verified by designing a model of a matrix consisting of 16 ultrasonic transducers with electrode attachments optimised by means of electronic switches in rows and columns and miniature transistor switches in the nodes of the matrix allowing to activate selected transducers. The results of measurements and simulations of parameters of the designed matrix show that it is suitable to be used in projection imaging of biological media as a sending probe. In to use the matrix as a universal sending or receiving probe, it was suggested to add further switches that would eliminate the undesired effect of crosstalks in case of switches used for toggling the transducers in the nodes of the matrix.
This paper presents results of evolutionary minimisation of peak-to-peak value of a multi-tone signal. The signal is the sum of multiple tones (channels) with constant amplitudes and frequencies combined with variable phases. An exemplary application is emergency broadcasting using widely used analogue broadcasting techniques: citizens band (CB) or VHF FM commercial broadcasting. The work presented illustrates a relatively simple problem, which, however, is characterised by large combinatorial complexity, so direct (exhaustive) search becomes completely impractical. The process of minimisation is based on genetic algorithm (GA), which proves its usability for given problem. The final result is a significant reduction of peak-to-peak level of given multi-tone signal, demonstrated by three real-life examples.
Reactive distillation (RD) has already demonstrated its potential to significantly increase reactant conversion and the purity of the target product. Our work focuses on the application of RD to reaction systems that feature more than one main reaction. In such multiple-reaction systems, the application of RD would enhance not only the reactant conversion but also the selectivity of the target product. The potential of RD to improve the product selectivity of multiple-reaction systems has not yet been fully exploited because of a shortage of available comprehensive experimental and theoretical studies. In the present article, we want to theoretically identify the full potential of RD technology in multiple-reaction systems by performing a detailed optimisation study. An evolutionary algorithm was applied and the obtained results were compared with those of a conventional stirred tank reactor to quantify the potential of RD to improve the target product selectivity of multiple-reaction systems. The consecutive transesterification of dimethyl carbonate with ethanol to form ethyl methyl carbonate and diethyl carbonate was used as a case study.
Saccharamyces cerevisia known as baker’s yeast is a product used in various food industries. Worldwide economic competition makes it a necessity that industrial processes be operated in optimum conditions, thus maximisation of biomass in production of saccharamyces cerevisia in fedbatch reactors has gained importance. The facts that the dynamic fermentation model must be considered as a constraint in the optimisation problem, and dynamics involved are complicated, make optimisation of fed-batch processes more difficult. In this work, the amount of biomass in the production of baker’s yeast in fed-batch fermenters was intended to be maximised while minimising unwanted alcohol formation, by regulating substrate and air feed rates. This multiobjective problem has been tackled earlier only from the point of view of finding optimum substrate rate, but no account of air feed rate profiles has been provided. Control vector parameterisation approach was applied the original dynamic optimisation problem which was converted into a NLP problem. Then SQP was used for solving the dynamic optimisation problem. The results demonstrate that optimum substrate and air feeding profiles can be obtained by the proposed optimisation algorithm to achieve the two conflicting goals of maximising biomass and minimising alcohol formation.
Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed. In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.