Application of Central Composite Design to Optimize Spawns Propagation

Despite the increased recognition of the nutritional value of the Oyster mushroom, coupled with its ability to tolerate a wide range of climatic conditions, its production is still at infancy stage with low adoption rate in Kenya. The low uptake could be attributed to the cost of spawns or lack of skills for spawns preparations coupled with poor knowledge on oyster mushroom consumption benefits. The objective of this study was to optimize Pleurotus ostreatus (Oyster mushroom) spawns production. To achieve the objective, the spawns propagation was optimized by varying the temperature level, sterilization time and culture media concentration in order to establish the feasible levels which minimized the days of mycelium full development using central composite designs. Based on the study findings, 26.29˚C, 17.36 minutes and 60.95 g/L of temperature level, sterilization time and culture media concentration levels respectively minimized the days to full coverage of mycelium in a petri dish. Central composite designs for controlling temperature, sterilization time and culture media concentration were recommended for spawns maximum production. A further research on multiple response optimizations aimed at achieving resistance to bacterial diseases and yield by varying the strain in the culture were recommended.

Investigation of ductile iron casting process parameters using Taguchi approach and response surface methodology

To find the optimized levels of various casting parameters in the ductile iron casting, various casting defects and the rejection rate were observed from a medium scale foundry. The controlled values of different casting parameters such as pouring temperature, inoculation, carbon equivalent, moisture content, green compression strength, permeability and mould hardness were selected. Three different melts of metal with 0.4wt.%, 0.6wt.%, and 0.8wt.% inoculation （Fe-Si-Mg alloy and post inoculant） were produced using a 1-ton capacity coreless medium frequency induction furnace. L-27 orthogonal array with 3-level settings were chosen for the analysis. Responses for each run were observed. The signal-to-noise （S/N） ratio for each run was calculated using the Taguchi approach, and the optimized levels of different casting parameters were identified based on the SIN ratio. The analysis of variance for the casting acceptance percentage concludes that inoculation is the most significant factor affecting the castings＇ quality with a contribution percentage of 44%; an increase in inoculation results in a significant improvement in acceptance percentage of ductile iron castings. The experiment results showed that with the optimized parameters, the rejection rate was reduced from 16.98% to 6.07%.

Multi-objective steady-state optimization of two-chamber microbial fuel cells

A microbial fuel cell(MFC)is a novel promising technology for simultaneous renewable electricity generation and wastewater treatment.Three non-comparable objectives,i.e.power density,attainable current density and waste removal ratio,are often conflicting.A thorough understanding of the relationship among these three conflicting objectives can be greatly helpful to assist in optimal operation of MFC system.In this study,a multiobjective genetic algorithm is used to simultaneously maximizing power density,attainable current density and waste removal ratio based on a mathematical model for an acetate two-chamber MFC.Moreover,the level diagrams method is utilized to aid in graphical visualization of Pareto front and decision making.Three biobjective optimization problems and one three-objective optimization problem are thoroughly investigated.The obtained Pareto fronts illustrate the complex relationships among these three objectives,which is helpful for final decision support.Therefore,the integrated methodology of a multi-objective genetic algorithm and a graphical visualization technique provides a promising tool for the optimal operation of MFCs by simultaneously considering multiple conflicting objectives.

The Networked Manufacturing Resources Optimizing Configuration System and Its Partners Selection Method

The three levels optimizing strategy is put forward for the networked manufacturing resources optimizing configuration, namely, the optimizing of a logical manufacturing process, the optimizing of simulation-based integration of process planning and scheduling, and the optimizing of networked production scheduling. Then, the web services-based architecture of networked manufacturing resources optimizing configuration is brought forward. Finally, the key algorithm of the networked manufacturing resources optimizing configuration is discussed, namely, the two phases manufacturing partners selection method, which including the group technology-based manufacturing resources pre-configuration and the genetic algorithm-based executable manufacturing process optimizing.

Energy Estimation and Optimization Platform for 4G and the Future Base Station System Early-Stage Design

There are already several power models to estimate the power consumption of base stations at system level. However, there is so far no model that can predict power consumption of the future base station designs based on algorithms and hardware selections with insufficient physical information. We present such an energy model for typical base stations. This model can help designers in estimating, evaluating and optimizing energy/power consumption of candidate designs in early design stages. The proposed model is verified by an LTE extreme scenario. The estimated results show that digital front-end, channel equalization and channel decoding are three major power greedy modules(consuming 39.4%, 16.3%, 13.4%) in a digital baseband subsystem. The power estimation error of the proposed power amplifier(PA) power model is 3.5%(macro cell). The major contribution of this paper is that the proposed models can rapidly estimate energy/power consumption of 4G and the future base stations(such as 5G) in early design stages with well acceptable precision, even without sufficient implementation information.

An improved window opening behavior model involving the division of the dummy variable’s interval level:Case study of an office building in Xi’an during summer

Window opening behavior significantly impacts indoor air quality,thermal comfort,and energy consumption.A field measurement was carried out in three typical rooms(a standard office,a meeting room and a smoking office)within an office building.The window state and the physical environment were continuously recorded during the measured periods.Three typical window opening behaviors were found in the measured samples,namely,active,moderate,and passive.The common logistic regression coefficient indicated that solar radiation exhibited the greatest effect on window opening behavior in the smoking office and standard office.Typically,window opening behavior in the meeting room was the most strongly correlated with time of the day,mainly because of the meeting schedule for occupants in the meeting room.This study discussed the dividing principles involved in setting the dummy variable interval level(discretizing continuous variables and dividing them into different intervals),and proposed a method to determine the optimal interval level of each variable.The improved model led to the increase in the prediction accuracy rate of the window being opened by 2.0%and 3.3%according to the comparison with the original model based on dummy variables and the common model based on continuous variables,respectively.This study can provide a reference value for simulating energy consumption in office buildings in the future.