Grain refining and modification are common foundry practice for improving properties of cast Al-Si alloys. In general, these types of treatments provide better fluidity, decreased porosity, higher yield strength and ductility. However, in practice, there are still some discrepancies on the reproducibility of the results from grain refining and effect of the refiner’s additions. Several factors include the fading effect of grain refinement and modifiers, inhomogeneous dendritic structure and non-uniform eutectic modification. In this study, standard ALCAN test was used by considering Taguchi’s experimental design techniques to evaluate grain refinement and modification efficiency. The effects of five casting parameters on the grain size have been investigated for A357 casting alloy. The results showed that the addition of the grain refiner was the most effective factor on the grain size. It was found that holding time, casting temperature, alloy type and modification with Sr were less effective over grain refinement.
An approach helpful when developing an optimized construction of a 6/4 type switched reluctance motor (SRM) is described in the paper. The analytical modeling procedure, based on the reluctance network method and analytical solution of an ordinary differential equation, enables applying a gradient optimization routine and better control of optimization process. The model allows for estimation of the efficiency, torque, and acoustic noise of the motor taking into account the magnetic non-linearity and the control algorithm to keep a constant input power. A bicriterial optimization routine has been applied to find optimal constructions. Eleven geometric and winding parameters are supposed to be the optimization quantities. Analyzed constructions – the initial one and the optimized ones, were validated by means of FEM calculations. The proposed approach can be employed in designing the SRM to be a drive motor in an electrical vehicle, at least as a first attempt.
A substantial quantity of research on muffler design has been restricted to a low frequency range using the plane wave theory. Based on this theory, which is a one-dimensional wave, no higher order wave has been considered. This has resulted in underestimating acoustical performances at higher frequencies when doing muffler analysis via the plane wave model. To overcome the above drawbacks, researchers have assessed a three-dimensional wave propagating for a simple expansion chamber muffler. Therefore, the acoustic effect of a higher order wave (a high frequency wave) is considered here. Unfortunately, there has been scant research on expansion chamber mufflers equipped with baffle plates that enhance noise elimination using a higher-order-mode analysis. Also, space-constrained conditions of industrial muffler designs have never been properly addressed. So, in order to improve the acoustical performance of an expansion chamber muffler within a constrained space, the optimization of an expansion chamber muffler hybridized with multiple baffle plates will be assessed. In this paper, the acoustical model of the expansion chamber muffler will be established by assuming that it is a rigid rectangular tube driven by a piston along the tube wall. Using an eigenfunction (higher-order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). Before the optimization process is performed, the higher-order-mode mathematical models of three expansion chamber mufflers (A-C) with various allocations of inlets/outlets and various chambers are also confirmed for accuracy. Results reveal that the STL of the expansion chamber mufflers at the targeted tone has been largely improved and the acoustic performance of a reverse expansion chamber muffler is more efficient than that of a straight expansion chamber muffler. Moreover, the STL of the expansion chamber mufflers will increase as the number of the chambers that separate with baffles increases.
The paper presents analysis of optimisation results of power system stabilizer (PSS) parameters when taking into account the uncertainty of mathematical model parameters of the power system (PS) elements. The Pareto optimisation was used for optimisation of the system stabilizer parameters. Parameters of five stabilizers of PSS3B type were determined in optimisation process with use of a genetic algorithm with tournament selection. The results obtained were assessed from the point of view of selecting the criterion function. The analysis of influence of the parameter uncertainty on the quality of the results obtained was performed.
Zinc plant residue is a hazardous waste which contains high quantity of nickel and other valuable metals. Process parameters such as reaction time, acid concentration, solid-liquid ratio, particle size, stirring speed and temperature for nickel extraction from this waste were optimized using factorial design. Main effects and their interactions were obtained by the analysis of variance ANOVA. Empirical regression model was obtained and used to predict nickel extraction with satisfactory results and to describe the relationship between the predicted results and the experiment results. The important parameters for maximizing nickel extraction were identifi ed to be a leaching time solid-liquid ratio and acid concentration. It was found that above 90% of nickel could be extracted in optimum conditions.
In this work, response surface optimization strategy was employed to enhance the biodegradation process of fresh palm oil mill effluent (POME) by Aspergillus niger and Trichoderma virens. A central composite design (CCD) combined with response surface methodology (RSM) were employed to study the effects of three independent variables: inoculum size (%), agitation rate (rpm) and temperature (°C) on the biodegradation processes and production of biosolids enriched with fungal biomass protein. The results achieved using A. niger were compared to those obtained using T. virens. The optimal conditions for the biodegradation processes in terms of total suspended solids (TSS), turbidity, chemical oxygen demand (COD), specific resistance to filtration (SRF) and production of biosolids enriched with fungal biomass protein in fresh POME treated with A. niger and T. virens have been predicted by multiple response optimization and verified experimentally at 19% (v/v) inoculum size, 100 rpm, 30.2°C and 5% (v/v) inoculum size, 100 rpm, 33.3°C respectively. As disclosed by ANOVA and response surface plots, the effects of inoculum size and agitation rate on fresh POME treatment process by both fungal strains were significant.
Mining ventilation should ensure in the excavations required amount of air on the basis of determined regulations and to mitigate various hazards. These excavations are mainly: longwalls, function chambers and headings. Considering the financial aspect, the costs of air distribution should be as low as possible and due to mentioned above issues the optimal air distribution should be taken into account including the workers safety and minimization of the total output power of main ventilation fans. The optimal air distribution is when the airflow rate in the mining areas and functional chambers are suitable to the existing hazards, and the total output power of the main fans is at a minimal but sufficient rate. Restructuring of mining sector in Poland is usually connected with the connection of different mines. Hence, dependent air streams (dependent air stream flows through a branch which links two intake air streams or two return air streams) exist in ventilation networks of connected mines. The zones of intake air and return air include these air streams. There are also particular air streams in the networks which connect subnetworks of main ventilation fans. They enable to direct return air to specified fans and to obtain different airflows in return zone. The new method of decreasing the costs of ventilation is presented in the article. The method allows to determine the optimal parameters of main ventilation fans (fan pressure and air quantity) and optimal air distribution can be achieved as a result. Then the total output power of the fans is the lowest which makes the reduction of costs of mine ventilation. The new method was applied for selected ventilation network. For positive regulation (by means of the stoppings) the optimal air distribution was achieved when the total output power of the fans was 253.311 kW and for most energy-intensive air distribution it was 409.893 kW. The difference between these cases showed the difference in annual energy consumption which was 1 714 MWh what was related to annual costs of fan work equaled 245 102 Euro. Similar values for negative regulation (by means of auxiliary fans) were: the total output power of the fans 203.359 kW (optimal condition) and 362.405 kW (most energy-intensive condition). The difference of annual energy consumption was 1 742 MWh and annual difference of costs was 249 106 Euro. The differences between optimal airflows considering positive and negative regulations were: the total output power of fans 49.952 kW, annual energy consumption 547 MWh, annual costs 78 217 Euro.
High distribution system power-losses are predominantly due to lack of investments in R&D for improving the efficiency of the system and improper planning during installation. Outcomes of this are un-designed extensions of the distributing power lines, the burden on the system components like transformers and overhead (OH) lines/conductors and deficient reactive power supply leading to drop in a system voltage. Distributed generation affects the line power flow and voltage levels on the system equipment. These impacts of distributed generation (DG) may be to improve system efficiency or reduce it depending on the operating environment/conditions of the distribution system and allocation of capacitors. For this purpose, allocating of distributed generation optimally for a given radial distribution system can be useful for the system outlining and improve efficiency. In this paper, a new method is used for optimally allocating the DG units in the radial distribution system to curtail distribution system losses and improve voltage profile. Also, the variation in active power load in the system is considered for effective utilization of DG units. To evidence the effectiveness of the proposed algorithm, computer simulations are carried out in MATLAB software on the IEEE-33 bus system and Vastare practical 116 bus system.
In this paper a scaling approach for the solution of 2D FE models of electric machines is proposed. This allows a geometrical and stator and rotor resistance scaling as well as a rewinding of a squirrel cage induction machine enabling an efficient numerical optimization. The 2D FEM solutions of a reference machine are calculated by a model based hybrid numeric induction machine simulation approach. In contrast to already known scaling procedures for synchronous machines the FEM solutions of the induction machine are scaled in the stator-current-rotor-frequency-plane and then transformed to the torque- speed-map. This gives the possibility to use a new time scaling factor that is necessary to keep a constant field distribution. The scaling procedure is validated by the finite element method and used in a numerical optimization process for the sizing of an electric vehicle traction drive considering the gear ratio. The results show that the scaling procedure is very accurate, computational very efficient and suitable for the use in machine design optimization.
The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.
In order to recover the low grade waste heat and increase system fuel economy for main engine 10S90ME-C9.2-TII(part load, exhaust gas bypass) installed on a 10000 TEU container ship, a non-cogeneration and single-pressure type of waste heat recovery system based on organic Rankine cycle is proposed. Organic compound candidates appropriate to the system are analyzed and selected. Thermodynamic model of the whole system and thermoeconomic optimization are performed. The saturated organic compound vapor mass flow rate, net electric power output, pinch point, thermal efficiency and exergy efficiency varied with different evaporating temperature are thermodynamically analyzed. The results of thermodynamic and thermoeconomic optimization indicate that the most appropriate organic compound candidate is R141b due to its highest exergy efficiency, biggest unit cost benefit and shortest payback time.
The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.
The optimization of cut-off grades is a fundamental issue for metallic ore deposits. The cut-off grade is used to classify the material as ore or waste. Due to the time value of money, in order to achieve the maximum net present value, an optimum schedules of cut-off grades must be used. The depletion rate is the rate of depletion of a mineral deposit. Variable mining costs are to be applied to the really excavated material, as some of the depletion can be left in-situ. Due to access constraints, some of the blocks that have an average grade less than the determined cut-off grade are left in-situ, some of them are excavated and dumped as waste material. Naturally, variable mining costs should be applied to the blocks of a mineral deposit that are actually excavated. The probability density function of an exponential distribution is used to find the portion of the depletion rate over the production rate that is to be left in-situ. As a result, inverse probability density function is to be applied as the portion of the depletion rate over the production rate that is to be excavated and dumped. The parts of a mineral deposit that are excavated but will be dumped as waste material incur some additional cost of rehabilitation that is to be included in the algorithm of the cut-off grades optimization. This paper describes the general problem of cut-off grades optimization and outlines the further extension of the method including various depletion rates and variable rehabilitation cost. The author introduces the general background of the use of grid search in cut-off grades optimization by using various depletion rates and variable rehabilitation cost. The software developed in this subject is checked by means of a case study.
Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
Energy and spectral efficiency are the main challenges in 5th generation of mobile cellular networks. In this paper, we propose an optimization algorithm to optimize the energy efficiency by maximizing the spectral efficiency. Our simulation results show a significant increase in terms of spectral efficiency as well as energy efficiency whenever the mobile user is connected to a low power indoor base station. By applying the proposed algorithm, we show the network performance improvements up to 9 bit/s/Hz in spectral efficiency and 20 Gbit/Joule increase in energy efficiency for the mobile user served by the indoor base station rather than by the outdoor base station.
In the paper the new constructions of robots, modern technologies of painting and newest methods of paint robots programming were presented. Fanuc P-250iA robot using to painting was characterized. The general characteristic of robot with controller R-30iA was demonstrated. The technology and the paint equipment applied to paint frames and load-carrying boxes was shown. The possibilities of simulation software Roboguide were presented exactly, which is a tool for robot environment simulation on a computer PC. Roboguide system application can reduce the programming time of robots and necessary programs optimization conducted before implementation to production.
Screw axis measurement methods obtain a precise identification of the physical reality of the industrial robots’ geometry. However, these methods are in a clear disadvantage compared to mathematical optimisation processes for kinematical parameters. That’s because mathematical processes obtain kinematical parameters which best reduce the robot errors, despite not necessarily representing the real geometry of the robot. This paper takes the next step at the identification of a robot’s movement from the identification of its real kinematical parameters for the later study of every articulation’s rotation. We then obtain a combination of real kinematic and dynamic parameters which describe the robot’s movement, improving its precision with a physical understanding of the errors.
In the paper an application of evolutionary algorithm to design and optimization of combinational digital circuits with respect to transistor count is presented. Multiple layer chromosomes increasing the algorithm efficiency are introduced. Four combinational circuits with truth tables chosen from literature are designed using proposed method. Obtained results are in many cases better than those obtained using other methods.
The paper presents a heuristic approach to the problem of analog circuit diagnosis. Different optimization techniques in the field of test point selection are discussed. Two new algorithms: SALTO and COSMO have been introduced. Both searching procedures have been implemented in a form of the expert system in PROLOG language. The proposed methodologies have been exemplified on benchmark circuits. The obtained results have been compared to the others achieved by different approaches in the field and the benefits of the proposed methodology have been emphasized. The inference engine of the heuristic algorithms has been presented and the expert system knowledge-base construction discussed.