Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Renewable energy forms have been widely used in the past decades highlighting a "green" shift in energy production. An actual reason behind this turn to renewable energy production is EU directives which set the Union's targets for energy production from renewable sources, greenhouse gas emissions and increase in energy efficiency. All member countries are obligated to apply harmonized legislation and practices and restructure their energy production networks in order to meet EU targets. Towards the fulfillment of 20-20-20 EU targets, in Greece a specific strategy which promotes the construction of large scale Renewable Energy Source plants is promoted. In this paper, we present an optimal design of the Greek renewable energy production network applying a 0-1 Weighted Goal Programming model, considering social, environmental and economic criteria. In the absence of a panel of experts Data Envelopment Analysis (DEA) approach is used in order to filter the best out of the possible network structures, seeking for the maximum technical efficiency. Super-Efficiency DEA model is also used in order to reduce the solutions and find the best out of all the possible. The results showed that in order to achieve maximum efficiency, the social and environmental criteria must be weighted more than the economic ones.

A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Conversion of plastic waste into CNTs using Ni/Mo/MgO catalystAn optimization approach by mixture experiment

A combustion technique is used to study the synthesis of carbon nano tubes from waste plastic as a precursor and Ni/Mo/MgO as a catalyst. The catalytic activity of three components Ni, Mo, MgO is measured in terms of amount of carbon product obtained. Different proportions of metal ions are optimized using mixture experiment in Design expert software. D-optimal design technique is adopted due to nonsimplex region and presence of constraints in the mixture experiment. The activity of the components is observed to be interdependent and the component Ni is found to be more effective. The catalyst containing Ni0.8Mo0.1MgO0.1 yields more carbon product. The structure of catalyst and CNTs are studied by using SEM, XRD, and Raman spectroscopy. SEM analysis shows the formation of longer CNTs with average diameter of 40-50 nm.

Difference variance dispersion graphs for comparing response surface designs with applications in food technology

Variance dispersion graphs have become a popular tool in aiding the choice of a response surface design. Often differences in response from some particular point, such as the expected position of the optimum or standard operating conditions, are more important than the response itself. We describe two examples from food technology. In the first, an experiment was conducted to find the levels of three factors which optimized the yield of valuable products enzymatically synthesized from sugars and to discover how the yield changed as the levels of the factors were changed from the optimum. In the second example, an experiment was conducted on a mixing process for pastry dough to discover how three factors affected a number of properties of the pastry, with a view to using these factors to control the process. We introduce the difference variance dispersion graph (DVDG) to help in the choice of a design in these circumstances. The DVDG for blocked designs is developed and the examples are used to show how the DVDG can be used in practice. In both examples a design was chosen by using the DVDG, as well as other properties, and the experiments were conducted and produced results that were useful to the experimenters. In both cases the conclusions were drawn partly by comparing responses at different points on the response surface.

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

A hydro-wood net-energy approach to hydro project design

An integrated approach to energy planning, when applied to large hydroelectric projects, requires that the energy-opportunity cost of the land submerged under the reservoir be incorporated into the planning methodology. Biomass energy lost from the submerged land has to be compared to the electrical energy generated, for which we develop four alternative formulations of the net-energy function. The design problem is posed as an LP problem and is solved for two sites in India. Our results show that the proposed designs may not be viable in net-energy terms, whereas a marginal reduction in the generation capacity could lead to an optimal design that gives substantial savings in the submerged area. Allowing seasonal variations in the hydroelectric generation capacity also reduces the reservoir size. A mixed hydro-wood generation system is then examined and is found to be viable.

Surrogate based design optimisation of composite aerofoil cross-section for helicopter vibration reduction

Design optimisation of a helicopter rotor blade is performed. The objective is to reduce helicopter vibration and constraints are put on frequencies and aeroelastic stability. The ply angles of the D-spar and skin of the composite rotor blade with NACA 0015 aerofoil section are considered as design variables. Polynomial response surfaces and space filling experimental designs are used to generate surrogate models of the objective function with respect to cross-section properties. The stacking sequence corresponding to the optimal cross-section is found using a real-coded genetic algorithm. Ply angle discretisation of 1 degrees, 15 degrees, 30 degrees and 45 degrees are used. The mean value of the objective function is used to find the optimal blade designs and the resulting designs are tested for variance. The optimal designs show a vibration reduction of 26% to 33% from the baseline design. A substantial reduction in vibration and an aeroelastically stable blade is obtained even after accounting for composite material uncertainty.

基于D稳定域和ITAE准则的主动队列管理算法

主动队列管理（active queue management，简称AQM）是网络拥塞控制的研究热点之一，其中的关键问题是如何设计反馈控制策略．提出一种新的基于D稳定域和时间乘以误差绝对值乘积积分（integral of time-weighted absolute error,简称ITAE）性能准则的比例-积分-微分（proportional-integral-differential，简称PID）优化设计方法（简称DITAE-PID），并用于AQM控制器的设计，控制闭环系统的理想动态性能．首先在复平面上设定一组理想的D稳定域，然后以ITAE为目标函数，通过数值优化算法求出控制器的参数，使得闭环系统的所有特征根都在D稳定域内，以降低排队延时，提高有效吞吐量．对比仿真实验结果表明孩算法能够预先探测和控制拥塞，有较好的鲁棒性，链路利用率更高，丢包率更小，平均队列长度更趋于期望值，同时，趋于期望队列长度的时间更短，其综合性能明显优于典型的随机早期探测（random early detection，简称RED）和比例-积分（proportional-integral，简称PI）算法．