Optimal Set Of Variables Research Articles

Performance of tuned tandem mass dampers for structures under the ground acceleration

It is widely acknowledged that the tuned mass damper (TMD) is one of the most effective and simplest passive control devices, but its limited control performance is still a troubling problem. In order to surmount the shortage of TMD, the tuned tandem mass dampers (referred herein to as TTMD) have been proposed for mitigating the undesirable oscillation of structures under the ground acceleration. Based on the formulation of the mode-generalized system in the specific vibration mode being controlled, the analytical expression is then derived for the dynamic magnification factor of the structure furnished with a TTMD. The optimum criterion can thereby be defined as minimization of the minimum values of the maximum dynamic magnification factor with a set of optimization variables embedded so as to give full play to the control device potential. The optimization implementation of TTMD is carried out by the MATLAB-based coding and debugging. For the purpose of a mutual authentication to the optimization results, three metaheuristic algorithms, namely, genetic algorithm, particle swarm optimization, and simulated annealing, are concurrently taken into consideration. Results demonstrate that the proposed TTMD endows with the superior stroke performance with respect to TMD.

A self-tuning ANN model for simulation and forecasting of surface flows

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.

The application of Taguchi methods to parameters optimization for preventing coagulation in artificial kidneys

This paper aims to illustrate an application of Taguchi method to identification of the setting optimized for factors affecting coagulation in a hemodialyzer as the benchmark of operations for improved quality and reduced coagulation. In this article, robust parameter designs based on Taguchi methods in association with discussions with the nursing staff apply to the study for investigating the key variables influential to hemodialysis. This paper attempt to improve the quality of hemodialysis by identification of the optimal set of variables and levels involved. The results show that the defect rate dropped from 35 to 8%. Thus, Taguchi methods that did improve the quality of hemodialysis are advised to apply to prevention of clotting during hemodialysis.

A bootstrapping soft shrinkage approach for variable selection in chemical modeling

In this study, a new variable selection method called bootstrapping soft shrinkage (BOSS) method is developed. It is derived from the idea of weighted bootstrap sampling (WBS) and model population analysis (MPA). The weights of variables are determined based on the absolute values of regression coefficients. WBS is applied according to the weights to generate sub-models and MPA is used to analyze the sub-models to update weights for variables. The optimization procedure follows the rule of soft shrinkage, in which less important variables are not eliminated directly but are assigned smaller weights. The algorithm runs iteratively and terminates until the number of variables reaches one. The optimal variable set with the lowest root mean squared error of cross-validation (RMSECV) is selected. The method was tested on three groups of near infrared (NIR) spectroscopic datasets, i.e. corn datasets, diesel fuels datasets and soy datasets. Three high performing variable selection methods, i.e. Monte Carlo uninformative variable elimination (MCUVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm partial least squares (GA-PLS) are used for comparison. The results show that BOSS is promising with improved prediction performance. The Matlab codes for implementing BOSS are freely available on the website: http://www.mathworks.com/matlabcentral/fileexchange/52770-boss.

Using a multiple response optimization approach to optimize the coefficient of performance

Response surface methodology was employed to optimize the efficiency of a refrigeration cycle demonstration unit using a multiresponse optimization approach. Statistically designed experiments were conducted to simultaneously minimize energy consumption and maximize the refrigeration effect of a compression refrigeration cycle. Regression models were fitted to refrigeration and electrical powers and optimal variable settings were identified using an easy-to-use optimization criterion. Results give confidence to apply the illustrated approach in academic and industrial settings, namely for optimizing equipment operation.

Selection of Geometric Design Variables for Fine Numerical Optimizations of Electrical Machines

This paper proposes an extended set of variables for numerical optimizations of tooth-coil permanent magnet synchronous machines (TC-PMSMs). The design variables for the extended set are chosen based on the magnetic asymmetries introduced by the saturated core under the loading conditions. The extended set allows to reach considerably higher values of torque density and significant reduction of torque ripple in comparison with a conventional set of optimization variables. The conventional and extended sets are compared on an example of an finite element method simulation of a double-layer winding surface permanent magnet-rotor TC-PMSM. A considerably better optimal machine is obtained when the extended set of variables is utilized. The paper showcases the design and the optimization of electrical machines using only free and open-source software.

Assessing changes in subjective and objective function from pre- to post-knee arthroplasty using the Cardiff Dempster–Shafer theory classifier

The purpose of this study is to assess changes in subjective and objective function from pre- to post-knee arthroplasty (KA) using a combined classifier technique. Twenty healthy adults (50–80 years) and 31 KA patients (39–81 years) were studied (4 weeks pre- and 6 months post-KA). Questionnaire measures of subjective pain, joint stability, activity and function were collected. Objective functional assessment included goniometry, ultrasound imaging and 3-D motion analysis/inverse modelling of gait and sit–stand. An optimal set of variables were used to classify function using the Cardiff Dempster–Shafer theory (DST) method. Out of sample accuracy of the classifiers ranged between 90% and 94% for segregating healthy individuals and pre-KA patients. Post-KA subjective function improved with 74% classified as healthy. However, there was minimal improvement in objective measures (23% classified as healthy). The novel use of Cardiff DST segregated KA patients from healthy individuals and estimated changes in function from pre- to post-surgery. KA patients had improved pain and function post-operation but objective knee joint measures remained different to healthy individuals.

Design and optimization of air bottoming cycles for waste heat recovery in off-shore platforms

This paper aims at comparing two methodologies to design an air bottoming cycle recovering the waste heat from the power generation system on the Draugen off-shore oil and gas platform. Firstly, the design is determined using the theory of the power maximization. Subsequently, the multi-objective optimization approach is employed to maximize the economic revenue, the compactness and the power production of the air bottoming cycle. The system compactness is assessed by introducing a detailed model of the shell and tube recuperator and including geometric quantities in the set of optimization variables. Findings indicate that using the power production, the volume of the recuperator and the net present value as objective functions the optimal pressure ratio (2.52) and the exhaust gas temperature (178.8°C) differ from the values (2.80 and 145.5°C) calculated using the theory of the power maximization. The highest net present value (2.8M$) is found for a volume of the recuperator of 128m3. Thus, it can be concluded that the multi-objective optimization approach enables extending the theory of power maximization bridging the gap between a mere optimization of the thermodynamic cycle and the practical feasibility of a power generation system.

Air quality prediction using optimal neural networks with stochastic variables

We apply recent methods in stochastic data analysis for discovering a set of few stochastic variables that represent the relevant information on a multivariate stochastic system, used as input for artificial neural network models for air quality forecast. We show that using these derived variables as input variables for training the neural networks it is possible to significantly reduce the amount of input variables necessary for the neural network model, without considerably changing the predictive power of the model. The reduced set of variables including these derived variables is therefore proposed as an optimal variable set for training neural network models in forecasting geophysical and weather properties. Finally, we briefly discuss other possible applications of such optimized neural network models.

An Optimal Power Flow Function to Aid Restoration Studies of Long Transmission Segments

This paper presents an optimal power flow function for aiding restoration studies of subsystems having long transmission segments. The method simultaneously computes an optimal set of variables which are critical for the subsystem restoration: the generation power plant high-side voltage, the minimum shunt reactor configuration and the maximum load to be safely energized. A set of overvoltage restoration scenarios following load rejection is constructed in such a way that the computed shunt reactor compensation becomes distributed along the transmission corridor. The whole problem is formulated as a single optimization problem whose solution is obtained by an optimal power flow, based on the Primal-Dual Interior Point Method. The described results for a restoration study on a system from practice confirm the effectiveness of the proposed method.

Sustainable development and public health: rating European countries

BackgroundSustainable development and public health quite strongly correlate, being connected and conditioned by one another. This paper therein attempts to offer a representation of Europe’s current situation of sustainable development in the area of public health.MethodsA dataset on sustainable development in the area of public health consisting of 31 European countries (formally proposed by the European Union Commission and EUROSTAT) has been used in this paper in order to evaluate said issue for the countries listed thereof. A statistical method which synthesizes several indicators into one quantitative indicator has also been utilized. Furthermore, the applied method offers the possibility to obtain an optimal set of variables for future studies of the problem, as well as for the possible development of indicators.ResultsAccording to the results obtained, Norway and Iceland are the two foremost European countries regarding sustainable development in the area of public health, whereas Romania, Lithuania, and Latvia, some of the European Union’s newest Member States, rank lowest. The results also demonstrate that the most significant variables (more than 80%) in rating countries are found to be “healthy life years at birth, females” (r2 = 0.880), “healthy life years at birth, males” (r2 = 0.864), “death rate due to chronic diseases, males” (r2 = 0.850), and “healthy life years, 65, females” (r2 = 0.844).ConclusionsBased on the results of this paper, public health represents a precondition for sustainable development, which should be continuously invested in and improved.After the assessment of the dataset, proposed by EUROSTAT in order to evaluate progress towards the agreed goals of the EU Sustainable Development Strategy (SDS), this paper offers an improved set of variables, which it is hoped, may initiate further studies concerning this problem.

Purification, Characterization and Application of a Cold- Adapted Phospholipase A1 from Bacillus Cereus Sp. AF-1

ABSTRACTThis paper reports the purification, characterization and application of an extracellular phospholipase A1 that functions at low temperature, from Bacillus cereus sp. AF-1 isolated from soil near the oil mill in Tianjin, China. The enzyme was purified 56-fold from fermentation supernatant, with 30.19 % recovery (specific activity 1195.24 U/mg). The purified enzyme has a molecular weight of 20 kDa. Using phospholipid as substrate, the enzyme showed maximum specific activity (Vmax) of 1.302 mmol·L−1mg−1 proteins with its corresponding Km value of 11.11 mg·mL−1. The enzyme was identified as phospholipase A1 by analyzing the free fat acid of enzymic hydrolysates which was extracted with a two-phase heptane-isopropyl alcohol—water system. The optimal temperature and pH for phospholipase A1 were 35°C and 7.5, respectively. The enzyme biocatalyst retained 85% of the initial activity after 10 weeks of storage at 4°C. The phosphorus content of the crude oil could be reduced to 7 mg·kg−1 when the optimal set of variables was: enzyme dosage of 0.4 mg kg−1 and reaction time of 3 h.

Study on Operating Characteristics of Power Plant with Dry and Wet Cooling Systems

The represent paper will study the performance of the power plant with the combination of dry and wet cooling systems in different operating conditions. A thermodynamic performance analysis of the steam cycle system was performed by means of a program code dedicated to power plant modeling in design operating condition. Then the off-design behavior was studied by varying not only the ambient temperature and relative humidity but also several parameters connected to the cooling performance, like the exhaust steam flow rate, the air cooling fan load and the number of operating cooling water pumps and cooling towers. The result is an optimum set of variables allowing the dry and wet cooling system be regulated in such a way that the maximum power is achieved and low water consumption.

Desirability function approach: A review and performance evaluation in adverse conditions

Adverse conditions in terms of quality of predictions and robustness are simulated to evaluate the ability of desirability-based methods for yielding compromise solutions with desired response's properties. The method's solutions are assessed at optimal variable settings with respect to bias, quality of predictions and robustness through optimization measures, and the usefulness of those measures to select the compromise solution is evaluated. Three examples with different features in terms of responses variance are used and the performance of various analysis methods is compared. Results show that a less sophisticated desirability-based method can compete with other methods designed to perform well under adverse conditions and that the optimization measures justify its use in real life problems.

Optimization of the Aqueous Enzymatic Extraction of Wheat Germ Oil Using Response Surface Methodology

AbstractA process of aqueous enzymatic extraction of wheat germ was carried out by a multi‐enzyme preparation consisting of cellulase, pentosanase, neutrase and fungal amylase (CPNF, 2:1:2:1 w/w/w/w). Hydro‐thermal heating (at 112 °C for 60 min) was more effective than oven‐drying regarding emulsified oil yield. Wheat germ was ground with a rate of 10,000 rpm for 90 s. The adding level (w/w) of multi‐enzyme preparation of CPNF was 1.6%. Response surface methodology was used to obtain the desired data in the process optimization. The optimal set of variables was water to wheat germ ratio (v/wt, mL/g) of 3.46, pH of 5.24, temperature of 48.49 °C and time of 6 h. The emulsified oil yield was 86.74% at the optimal levels of the tested factors. Compared with organic solvent extracted oil, the content of free fatty acid of AEE extracted oil was higher and the color was slightly darker, while the peroxide value was lower and the oxidative stability was higher owing to high content of α‐tocopherol. This technique for recovering oil from fresh wheat germ with enzymes is a significant improvement in both oil yield and quality over the traditional organic solvent process.

Wet and dry cooling systems optimization applied to a modern waste-to-energy cogeneration heat and power plant

In Brescia, Italy, heat is delivered to 70% of 200.000 city inhabitants by means of a district heating system, mainly supplied by a waste to energy plant, utilizing the non recyclable fraction of municipal and industrial solid waste (800,000 tons/year, otherwise landfilled), thus saving annually over 150,000 tons of oil equivalent and over 400,000 tons of CO 2 emissions. This study shows how the performance of the waste-to-energy cogeneration plant can be improved by optimising the condensation system, with particular focus on the combination of wet and dry cooling systems. The analysis has been carried out using two subsequent steps: in the first one a schematic model of the steam cycle was accomplished in order to acquire a knowledge base about the variables that would be most influential on the performance. In the second step the electric power output for different operating conditions was predicted and optimized in a homemade program. In more details, a thermodynamic analysis of the steam cycle, according to the design operating condition, was performed by means of a commercial code (Thermoflex ©) dedicated to power plant modelling. Then the off-design behaviour was investigated by varying not only the ambient conditions but also several parameters connected to the heat rejection rate, like the heat required from district heating and the auxiliaries load. Each of these parameters has been addressed and considered in determining the overall performance of the thermal cycle. After that, a complete prediction of the cycle behaviour was performed by simultaneously varying different operating conditions. Finally, a Matlab © computer code was developed in order to optimize the net electric power as a function of the way in which the condensation is operated. The result is an optimum set of variables allowing the wet and dry cooling system to be regulated in such a way that the maximum power is achieved. The best strategy consists in using the maximum amount of heat rejection in the wet cooling system to reduce the operational cost of the dry one.

MINLP optimization of mechanical draft counter flow wet-cooling towers

In this paper, the problem of the optimal design of mechanical draft counter flow cooling towers that meets a set of specified constraints is formulated as a mixed-integer nonlinear programming (MINLP) problem. The Merkel's method is used to specify the characteristic dimensions of cooling towers, together with empirical correlations for the loss and overall mass transfer coefficients in the packing region of the tower. Water-to-air mass ratio, water mass flow rate, water inlet and outlet temperatures, operational temperature approach, type of packing, type of draft, height and area of the tower packing, total pressure drop of air flow, power consumption of the fan, and water consumption provide the set of optimization variables. The MINLP problem is formulated so as to minimize the total annual cost of the cooling tower. The performance of the proposed procedure is shown with the solution of six examples.

Plasma transferrin receptor helps to predict iron deficiency in the anemia of chronic disease.

A recent study by Ahluwalia and colleagues used a discriminant statistical analysis approach to determine that a combination of serum ferritin, plasma transferrin receptor concentration, and erythrocyte sedimentation rate was the optimal set of variables for differentiating iron deficiency and the anemia associated with chronic disease in a group of elderly women. Iron deficiency was defined as a significant response in hemoglobin concentration after iron supplementation. The findings of this study suggest that iron deficiency can be relatively common among elderly anemic women with rheumatoid arthritis. Use of these three biochemical measures should be clinically useful to differentiate iron deficiency in the anemia of chronic disease.

A chaotic serpentine mixer efficient in the creeping flow regime: from design concept to optimization

Motivated by the three-dimensional serpentine channel (Liu et al. in J Microelectromech Syst 9:190-197, 2000), we introduce a chaotic serpentine mixer (CSM) and demonstrate a systematic way of utilizing a mapping method to find out an optimal set of design variables for the CSM. One periodic unit of the mixer has been designed to create two streamlines portraits crossing each other. As a preliminary study, flow characteristics and mixing in the original serpentine channel has been reinvestigated. The working principle of the CSM is demonstrated via a particle-tracking method. From the design principle and the flow characteristics of the CSM, we choose three key design variables with an influence on mixing. Then, simulations for all possible combinations of the variables are carried out. At proper combinations of the variables, almost global chaotic mixing is observed in the Stokes flow regime. The design windows obtained can be used to determine an optimal set of the variables to fit with a specific application.

Choosing the best set of variables in regression analysis using integer programming

This paper is concerned with an algorithm for selecting the best set of s variables out of k(> s) candidate variables in a multiple linear regression model. We employ absolute deviation as the measure of deviation and solve the resulting optimization problem by using 0-1 integer programming methodologies. In addition, we will propose a heuristic algorithm to obtain a close to optimal set of variables in terms of squared deviation. Computational results show that this method is practical and reliable for determining the best set of variables.