Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

A solid modeler based ball-end milling process simulation

A system for geometric and physical simulation of the ball-end milling process using solid modeling is presented in this paper. A commercially available geometric engine is used to represent the cutting edge, cutter and updated part. The ball-end mill cutter modeled in this study is an insert type ball-end mill and the cutting edge is generated by intersecting an inclined plane with the cutter ball nose. The contact face between cutter and updated part is determined from the solid model of the updated part and cutter solid model. To determine cutting edge engagement for each tool rotational step, the intersections between the cutting edge with boundary of the contact face are determined. The engaged portion of the cutting edge for each tool rotational step is divided into small differential oblique cutting edge segments. Friction, shear angles and shear stresses are identified from orthogonal cutting data base available in the open literature. For each tool rotational position, the cutting force components are calculated by summing up the differential cutting forces. The instantaneous dynamic chip thickness is computed by summing up the rigid chip thickness, the tool deflection and the undulations left from the previous tooth, and then the dynamic cutting forces are obtained. For calculating the ploughing forces, Wu's model is extended to the ball-end milling process [21]. The total forces, including the cutting and ploughing forces, are applied to the structural vibratory model of the system and the dynamic deflections at the tool tip are predicted. The developed system is verified experimentally for various up-hill and down-hill angles.     

A variable neighborhood binary particle swarm algorithm for cell layout problem

In this paper, a new mathematical model is proposed for inter- and intra-cell layout problem in cellular manufacturing system. A binary particle swarm optimization algorithm with a new heuristic approach for satisfying the constraints of model is implemented to solve the proposed model. Also, a variable neighborhood search is applied for finding better local optimal solutions. Some sensitivity analyses are carried out to find the best values of PSO parameters. To prove the efficiency of the proposed algorithm, various numerical examples in small, medium, and large sizes in both perfect geometric shapes and random placements are generated and solved.     

Volatile organic compound (VOC) adsorption on material: influence of gas phase concentration, relative humidity and VOC type

This paper presents the results of a factorial experiment design analysis to investigate volatile organic compounds (VOC) adsorption on a ceiling tile. The impacts of three factors, VOC gas phase concentration, relative humidity, and VOC type, as single parameters and as a combination, on adsorption have been investigated. Cyclohexane, toluene, ethyl acetate, isopropyl alcohol and methanol were the five VOCs used in this study. A factor significant level was determined through evaluating its F value and comparing it with the critical value of F distribution at 95% confidence level. It was found that: (i) neither the relative humidity and gas phase concentration nor any interaction effect between them had significant impacts on toluene adsorption on the ceiling tile; (ii) the adsorption isotherm appeared to be linear for the non-polar compounds and non-linear for the semi-polar and polar compounds; (iii) no significant impact of relative humidity on adsorption was observed for most VOC compounds except for methanol; and (iv) the ceiling tile had the highest adsorption capacity toward the polar compounds, followed by the aromatic compounds and aliphatic compounds. In addition, the statistical analysis regarding the experimental results of toluene as a single compound or as a part of a mixture showed that toluene adsorption capacity on the ceiling tile as a single compound was higher than as a part of a mixture. PRACTICAL IMPLICATIONS: Building materials and furnishings may act as source and sink of VOCs in the indoor environment. In this study, a factorial experiment design analysis technique was used to show the impact of three factors, VOC gas phase concentration, relative humidity, and VOC type, as single parameters and as a combination, on the adsorption process (sink effect). The aim was to better understand the interaction between these parameters and to verify the common assumptions made in the model development and measurement of indoor air quality.     

Multiobjective optimization of building design using TRNSYS simulations,genetic algorithm,and Artificial Neural Network

Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.     

A software framework for model predictive control with GenOpt

There is a growing interest in integrated control strategies for building systems with numerous responsive elements, such as solar shading devices, thermal storage and hybrid ventilation systems, both for energy efficiency and for demand response. Model predictive control is a promising way of approaching this challenge. This paper presents a flexible software framework for model predictive control using GenOpt, along with a modified genetic algorithm developed for use within it, and applies it to a case study of demand response by zone temperature ramping in an office space. Various areas for further research and development using this framework are discussed.     

A novel approach to enhance outdoor air quality: Pedestrian ventilation system

Higher population density has altered the cities' old landscape with dense areas consisting of high-rise buildings. As a result, detrimental phenomena appeared inside modern cities that threatened the inhabitant's health and comfort. Among these phenomena, the Urban Heat Island (UHI) is known as the most harmful side effect of the urbanization which affects the Outdoor Air Quality (OAQ).     

Evaluation of Four Tree Algorithms in Predicting and Investigating the Changes in Aquifer Depth

One of the key elements in improved management and better planning for aquifer maintenance is the ability to predict changes in aquifer depth. In order to forecast changes in aquifer depth in Qazvin plain, four methods, including Classification and Regression Tree (CART), Reduced Error Pruning Trees (RepTree), M5-Pruned (M5P), and M5Rule, were used in this work. The absolute mean error (MAE) and coefficient of determination (R2) data show that the CART algorithm performs better than other algorithms at forecasting changes in aquifer depth. The CART algorithm's prediction findings showed that the aquifer's behavior in the two seasons was entirely different. In the first stage, which began in November and continued through April, there was an annual average depth of 0.045 m. The aquifer depth has been greatly influenced by rising precipitation and falling air temperature. The aquifer experiences an average decline of 0.15 m in the second portion, which runs from May to October. Aquifer depth has significantly decreased as a result of declining natural water supplies and rising agricultural water use. It is advised to utilize a crop scheme with reduced water need when rainfall reduces due to the strong effect of changes in aquifer depth from rainfall with a delay of one to three months ago.

     

Reducing Dangerous Effects of Unsymmetrical Dimethylhydrazine as a Liquid Propellant by Addition of Hydroxyethylhydrazine—Part I: Physical Properties

In this work, the effect of low volatile hydroxyethylhydrazine (HEH) as a solute on unsymmetrical dimethylhydrazine (UDMH) has been studied in order to reduce harmful effects of UDMH vapors. Desirable physical properties of binary mixtures UDMH/HEH have been measured and compared to pure UDMH. These properties include boiling point, viscosity, density, and vapor pressure that are important for using binary mixtures of UDMH/HEH as less dangerous liquid propellants. Due to the formation of strong hydrogen bonding between UDMH and HEH, the volatility of UDMH has been reduced appreciably upon the addition of HEH. It is indicated that the measured physical properties may deviate significantly compared to corresponding predicted values. Binary mixtures of UDMH/HEH can also react spontaneously in contact with nitrogen tetroxide (NTO) and red fuming nitric acid (RFNA), so they can be called hypergolic propellants.     

Genetic algorithm application for matching ordinary black oil PVT data

In the study of reservoirs, it is vital that we have a realistic physical model of the reservoir fluid that accurately describes the hydrocarbon system and its properties. The available equations of state (EOS) to model the fluid phase behavior have some inherent deficiencies that may cause erroneous predictions for real reservoir fluids, so these models should be tuned against experimental data by adjusting some parameters. Since there are many matching parameters, tuning the EOS against experimental data is a tedious and difficult work. In this study, a genetic algorithm as an optimization technique is used to solve this regression problem. This study presents a new method that uses a specially designed genetic algorithm to search for suitable regression parameters to match the EOS against measured data. The proposed method has been tested on three real black oil samples. The results show the surprising performance of the developed genetic algorithm to match the experimental data of the selected fluid samples. The main advantage of the used method is its high speed in finding a solution. Also, finding more than one solution, working automatically, confining the role of experts to the last stage, reducing costs and having the possibility of evaluating the different situations are the other advantages of this method to match ordinary black oil PVT data and makes it an ideal method to implement as an automatic EOS tuning algorithm for black oils.