“Energy Tower” combined with pumped storage and desalination: Optimal design and analysis

The number of computed tomography (CT) systems in operation in Japan is approximately 4.3 times higher than that of the OECD average. However, CT systems are expensive, and thus, a heavy financial burden for hospital management. We calculate the annual net profits from CT introduction in Japan for single-slice CT (SSCT), multi-slice CT (MSCT), number of hospital beds, and prefecture. We also analyze the factors that affect CT profitability. First, the annual income per CT in operation is estimated for 2011. Second, the annual costs per CT are calculated as the sum of depreciation, maintenance, and labor costs. Finally, the annual net profits per CT are estimated for SSCT and MSCT, the number of hospital beds, and prefecture. A correlation analysis between the annual net profits, population, and number of physicians per CT equipment is used to determine the determinants of the net CT profits by prefecture. Our results show that, for hospitals with fewer than 100 beds, the annual net CT profits are higher for SSCT than MSCT, and vice versa for hospitals with at least 100 beds. Both SSCT and MSCT increased profits as the number of hospital beds increased. The annual net CT profits per prefecture are USD −12,105 for SSCT and USD 87,233 for MSCT, on average. The annual net profits per prefecture and population per CT show positive correlations with both SSCT and MSCT, as do the annual net profits per prefecture and number of physicians per CT. Thus, choosing high-performance MSCT is advantageous in terms of profitability in facilities with at least 100 beds. Additionally, CT profitability presumably affects the balance between the number of introduced CTs, population per CT, and number of physicians per CT.

Combined utilization of electricity and thermal storages in a highly renewable energy system within an island society

Recently, methods for computing D-optimal designs for population pharmacokinetic studies have become available. However there are few publications that have prospectively evaluated the benefits of D-optimality in population or single-subject settings. This study compared a population optimal design with an empirical design for estimating the base pharmacokinetic model for enoxaparin in a stratified randomized setting. The population pharmacokinetic D-optimal design for enoxaparin was estimated using the PFIM function (MATLAB version 6.0.0.88). The optimal design was based on a one-compartment model with lognormal between subject variability and proportional residual variability and consisted of a single design with three sampling windows (0-30 min, 1.5-5 hr and 11-12 hr post-dose) for all patients. The empirical design consisted of three sample time windows per patient from a total of nine windows that collectively represented the entire dose interval. Each patient was assigned to have one blood sample taken from three different windows. Windows for blood sampling times were also provided for the optimal design. Ninety six patients were recruited into the study who were currently receiving enoxaparin therapy. Patients were randomly assigned to either the optimal or empirical sampling design, stratified for body mass index. The exact times of blood samples and doses were recorded. Analysis was undertaken using NONMEM (version 5). The empirical design supported a one compartment linear model with additive residual error, while the optimal design supported a two compartment linear model with additive residual error as did the model derived from the full data set. A posterior predictive check was performed where the models arising from the empirical and optimal designs were used to predict into the full data set. This revealed the "optimal" design derived model was superior to the empirical design model in terms of precision and was similar to the model developed from the full dataset. This study suggests optimal design techniques may be useful, even when the optimized design was based on a model that was misspecified in terms of the structural and statistical models and when the implementation of the optimal designed study deviated from the nominal design.

IntroductionIn recent years, SMPMSM is quietly famous compared with other conventional motors in industrial applications due to higher efficiency, higher reliability, low cost, and smooth torque [1]–[3]. In general, several objective optimizations need to be consider, which required an optimization technique for an electrical motor. This paper deals with an optimal design of a surface-mounted permanent magnet synchronous machine (SMPMSM) with an exact analytical subdomain model by using a genetic algorithm method. To analyze the characteristic of permanent magnet (PM) motors, the classical optimization method, such as the finite element method (FEM), is intensively used. However, FEM has several time problems that require a longer computational time to evaluate the performance of PM motors. This problem can be overcome by using a genetic algorithm (GA) method combined with a subdomain model (SD), which developed an improved performance of SMPMSM, for instance, total harmonic distortion (THDv) and phase back-EMF. In this design, a three-phase 12-slot/8-pole PM motor is established with an exact SD model with radial magnetization patterns. Then, the GA ensemble with SD model to search the optimality of SMPMSM machine design. In the final analysis, the optimal new design of SMPMSM demonstrated by comparing with the initial design that is investigated by FEM. The result of induced back-EMF, and magnetic flux density of optimal design is compared with the initial design to show the advantages of GA optimization method.Analytical Model, Genetic Algorithm and Parameter of optimizationThe purpose of this analysis is to investigate the optimization design of a three-phase 12-slot/8-pole SMPMSM with a radial magnetization pattern. This optimization problem has been solved by obtains the minimum THDv as a fitness function. In order to process the optimization design of SMPMSM, several parameters need to be optimized i.e magnet pole-pitch, αp and slot-opening angle, boa. The roulette method is used to select the objective function of optimization with 50 maximum generations per iteration. The cross and mutation probability need to be set which requires linear and uniform recombination which is 0.89 and 0.025 respectively. The parameters of 12-slot/8-pole SMPM motor as follows: radius of the rotor, Rr = 23mm, the radius of PM surface Rm = 26mm, the inner radius of the stator Rsb = 42.5mm, axial length, la = 50mm, air-gap length, lg = 1mm, remanence of the magnet, Br, 1.12T, relative coil permeability, µr = 1.05 with the number of pole pairs, Np is 8 and number of stator slots, Ns is 12.The result, Discussion and ConclusionThe phase back electromotive force has a significant relationship with flux-linkage because phase back electromotive force is derived from the rate of change of flux-linkage. In Fig. 1. show the waveform of phase back electromotive force against rotor position of initial and optimal design in radial magnetization patterns. The initial design of phase back electromotive force waveform shows more trapezoidal compare with the optimal design which has a sinusoidal waveform. The comparison of total harmonic distortion between the initial and optimal design of SMPMSM are demonstrated in Fig. 2 below. The result from Fig.2. shows the highest total harmonic distortion of 12-slot/8-pole motor is an initial design which is 11.34% compared with the optimal design where 1.3%. The magnitude of harmonic in phase back electromotive force |En| of initial design shows 22.24 V compare to optimal design which is 17.4 V. This shows a good agreement that the optimal motor design gives lowest total harmonic distortion compare with the initial design. The optimizes of SMPMSM motor with the following parameters are given in Table 1. **

Background: Aloe vera has been used as a medicinal plant for several hundred years. Aloe vera gel is a rich source of certain bioactive polysaccharides which are essential for immune-therapic activities. Aloe vera gel is prone to bacterial activities which could reduce its storage life due to high water content (97-99%). Drying is the main operation for the dehydration of aloe vera gel but it could significantly affect the physicochemical and structural properties of dried aloe vera gel which in turn may alter its functionality. Individual drying processes like hot air and microwave drying, often fails to retain the quality of dried gel. Methods: Dehydration of aloe vera gel was performed using hot air, microwave-assisted, and hybriddrying processes towards the retention of its physicochemical and functional properties and, the statistical data analysis was performed by ANOVA to understand the effect of drying conditions on its quality. Results: Among all drying techniques, ‘centrifugation followed by freeze-drying’, and ‘centrifugation followed by hot air drying’ showed the highest carbohydrate and protein retention in dried aloe vera gel of 91.8, 82.3% and 90.9, 85.6%, respectively. Microwave input power did not alter the water retention capacity, but its higher value reduced both swelling property and fat adsorption capacity. However, all these functional properties gradually fell down with the increase in temperature of hot air drying. Conclusion: Carbohydrate, protein contents, and functional properties were sensitive to hot air drying temperature (>70 °C) and microwave power input in microwave-assisted drying (>160W). Retention of higher swelling and water retention capacity in hybrid techniques implied that the structural modification of the dried matrix was less affected. Keywords: Aloe vera gel, dehydration, hot air drying, microwave, hybrid drying techniques, functional properties.

Development of healthy crispy carrot snacks using sequential infrared blanching and hot air drying method

In order to find optimum and reliable designs for hydraulic water retaining structures (HWRSs), a reliability based optimum design (RBOD) model was used to quantify uncertainty in estimates of seepage characteristics due to uncertainty in heterogeneous hydraulic conductivity (HHC). This included incorporating reliability measures into minimum-cost HWRS designs and utilising a multi-realisation optimisation technique based on various stochastic ensemble surrogate models. To improve the efficiency of the RBOD model and the direct search optimisation solver, a multi-objective multi-realisation optimisation (MOMRO) model was employed. Some of the stochastic optimisation constraints could be formulated as a second objective function to be minimised in the MOMRO model. This can significantly improve the search efficiency of the multi-objective non-dominated sorting genetic algorithm-II (NSGA-II) that was used, and help determine more feasible candidate solutions in the search space. Gaussian process regression was used to develop the surrogate models, which were trained on numerous datasets created from numerical seepage simulations. The effect of uncertainty was also considered for other HWRS safety factors and conditions, such as overturning, flotation, sliding and eccentric loading. The results demonstrate that uncertainty in HHC estimates significantly impacts optimum HWRS design. Therefore, deterministic optimum solutions that are created based on expected values of hydraulic conductivity are not adequate for reliable HWRS design. The developed MOMRO model, which was based on an ensemble approach, addresses some of the uncertainty in HHC values that affects HWRS design. Also, the MOMRO technique improves the efficiency of the optimisation search process and facilitates a direct search process to provide many optimum alternatives.Highlights The uncertainty in HHC affects the optimum HWRS design. MOMR is used to quantify the reliability based on stochastic ensemble surrogate models. The MOMR technique improves the direct search optimization process based NSGA-II. Exit gradient is influenced by the uncertainty of HHC and affects the HWRS optimum designs.

Sampling design optimization for geostatistical modelling and prediction

Hot air (HA) drying of banana has low drying efficiency and results in undesirable product quality. The objectives of this research were to investigate the feasibility of infrared (IR) heating to improve banana drying rate, evaluate quality of the dried product, and establish models for predicting drying characteristics. Banana slices of 5 mm and 8 mm thickness were dried with IR and HA at product temperatures of 60C, 70C and 80C. Banana drying characteristics and changes in residual polyphenol oxidase (PPO), Hydroxymethylfurfural (HMF), color, moisture content (MC) and water activity during the treatments were investigated. Results showed that significant moisture reduction and higher drying rates were achieved with IR drying compared to HA drying in the early stage. The drying data could be fitted to the Page model for accurate prediction of MC change for IR and HA drying with mean R2 of 0.983. It was noted that enzyme inactivation occurred more quickly with IR than with HA drying. A unique response of PPO under IR and HA drying was revealed. IR heating of banana inactivated PPO within the first 20 min of drying at 60C, 70C and 80C, while PPO was first activated before inactivation at 60C and 70C drying with HA. The highest HMF content occurred in banana slices with 5 mm thickness dried with IR at a product temperature of 80C. It is therefore recommendable to dry banana with IR at product temperature of 70C or below to preserve the product quality. These findings are new and provide more insight in the application of IR heating for drying banana for improved drying rate and product quality.

To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

Hot air‐assisted radio frequency drying (HA‐RF) was performed to dry avocados and produce avocado powder. An electrode gap of 80 mm and sample thickness of 2 cm were selected as the ideal HA‐RF drying parameters. The HA‐RF drying kinetics were compared with hot air (HA) drying. The HA‐RF drying resulted in a faster and shorter drying process with the higher drying rate than HA drying. HA‐RF drying provided a 100% reduction in drying time (from 180 to 90 min) compared to the HA drying. The quality attributes of avocado powder obtained by HA‐RF drying were also assessed by comparing with HA and freeze‐dried samples. The color values, total phenolic content (TPC), antioxidant activity, porosity, dispersibility, solubility, caking degree, and hygroscopicity were affected by drying methods. The HA‐RF drying resulted in higher TPC (170.91 mg GAE/100 g dw) and antioxidant activity (39.39%) in avocado powder than HA drying (154.74 mg GAE/100 g dw and 30.09%, respectively). On the other hand, freeze‐dried samples had the highest TPC (202.05 mg GAE/100 g dw) and antioxidant activity (52.65%). The HA‐RF‐dried products exhibited the lowest color change (6.49), Carr index (21.43%), Hausner ratio (1.27), and wettability (2.07 s) compared to the HA and freeze dried samples. When all quality characteristics were considered, the HA‐RF drying method was advanced to the other methods, especially in fast drying time, and was found to be the best method for producing avocado powder.Practical ApplicationsThis study explored the effect of drying methods (HA‐RF drying and HA drying) on the drying characteristics and quality of dried avocado powder and its comparison with freeze drying. The study showed that HA‐RF drying resulted in a shorter processing time than other drying methods. The HA‐RF‐dried powder quality was comparable to that of a freeze‐dried one.

Drying kinetics and quality attributes of jujube (Zizyphus jujuba Miller) slices dried by hot-air and short- and medium-wave infrared radiation

Citrus reticulata Blanco cv. Dahongpao is a traditional Chinese citrus variety. Due to the high investment in storage and transport of Citrus reticulata Blanco cv. Dahongpao and the lack of market demand, the fresh fruit is wasted. The processing of fresh fruit into fruit drinks can solve the problem of storage and transport difficulties and open up new markets. Investigating the effects of different drying processes (hot air, freeze, and spray drying) on fruit powders is a crucial step in identifying a suitable production process. The experiment measured the effects of different drying methods (hot air drying, freeze drying, and spray drying) on the nutrient, bioactive substance, and physical characteristics of fruit powder. This study measured the influence of three different drying methods (hot air, freeze, and spray drying) on the nutritional, bioactive substance, and physical characteristics of fruit powder. The results showed that compared to vacuum freeze-drying at low temperature (-60 °C) and spray-drying at high temperatures (150 °C), hot air drying at 50 °C produced fruit powder with superior nutritional quality, higher levels of active substances, and better physical properties. Hot air drying produced fruit powder that had the highest content of amino acids (11.48 ± 0.08 mg/g DW), vitamin C (112.09 ± 2.86 μg/g DW), total phenols (14.78 ± 0.30 mg/g GAE DW), total flavonoids (6.45 ± 0.11 mg/g RE DW), organic acids, and antioxidant activity capacity. Additionally, this method yielded the highest amounts of zinc (8.88 ± 0.03 mg/Kg DW) and soluble sugars, low water content, high solubility, and brown coloration of the fruit powder and juice. Therefore, hot air drying is one of the best production methods for producing high-quality fruit powder in factory production.

Citrus ‘Hallabong’ powders were prepared using hot air and freeze drying methods, and their physicochemical and flow properties were measured. The yields of the powders from whole fruit and pressed cake by hot air drying method were 17.5 and 22.0%, while those by freeze drying method were 20.0 and 22.5%, respectively. Vitamin C was high in freeze-dried whole fruit powders (220.8–364.7 mg/100 g) compared with those in hot air-dried ones (80.1–114.6 mg/100 g). Browning index of freeze-dried powders was significantly lower than those of hot air-dried ones. Bulk densities, compaction densities, and Hausner ratios of the powders were significantly higher in freeze drying method compared with hot air drying method. Water solubilities and hygroscopicities of freeze-dried powders were higher than those of hot air-dried ones. In conclusion, ‘Hallabong’ powders can be made using freeze drying method with high quality in terms of vitamin C content, color, and water solubility.

Endwall contour optimization design utilized to improve the aerodynamic performance has impact on the flow field near rim seal, and it consequently affects the sealing performance. Combined with Kriging agent model, NSGA-II genetic algorithm and CFD which solves the three-dimensional unsteady Reynolds-averaged Navier–Stokes equations coupled with a fully developed shear stress transport turbulent model, this paper constructs an optimization design system for turbine non-axisymmetric endwall contour design in order to improve aerodynamic efficiency and sealing effectiveness simultaneously. The numerical method for predicting the flow in wheel-space and sealing performance of turbine rim seal is validated on the basis of published experimental data. The base flat endwall design, and two optimal endwall contour designs are selected to investigate the aerodynamic and sealing performance. The results show that the aerodynamic and sealing performance can be improved at the same time. Compared with the base flat endwall design, the total-to-total efficiency is increased by 0.23% and the sealing effectiveness is increased by 11.49% for optimal design 1 at optimal design point. Optimal design 1 can significantly reduce the aerodynamic loss and for different sealing flow rates, the total-to-total efficiency is greater than that of base flat endwall design. Compared with the base flat endwall design, the total-to-total efficiency is increased by 0.15% and the sealing effectiveness is increased by 15.43% for optimal design 2 at optimal design point. The influence of endwall contour on sealing effectiveness is obvious when the cooling flow rate is small. The work of this paper provides a reference for the design of non-axisymmetric endwall with comprehensive optimization of turbine aerodynamic and rim sealing performance.