Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.     

Improved Krill Algorithm for Reservoir Operation

Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study introduces an improved version of krill algorithm for reservoir operation. The algorithm is based on adding an onlooker search mechanism to avoid being trapped in local optima and then updating its position. The new krill algorithm is tested using a case study for irrigation management. The computation time is 33 s for the new algorithm but is 54, 59, and 60 s for krill algorithm, particle swarm optimization and genetic algorithm, respectively. Also, the improved krill algorithm can meet 97% of irrigation demands and has the lowest value of vulnerability index among genetic algorithm, particle swarm optimization, and simple krill algorithm. Also, the average solution of improved krill algorithm is close to the global solution. Results indicate that the improved krill algorithm has high potential for application in water resource management.     

An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure

In this paper, a novel digital predistorter design based on the Hammerstein structure is proposed in order to linearize radio frequency power amplifiers. A genetic algorithm optimization method has been proposed to accurately identify the coefficients of a Wiener model for the power amplifier. Digital predistorter design based on the proposed Hammerstein model has been carried out according to the accurate Wiener model. The validation of the suggested model is carried out using the simulation of the power amplifier and the digital predistortion excited by 64QAM signals in the advanced design system software. According to the simulation results, the criterion of an adjacent channel power ratio decreased by about 16 dB. The simulation results show the adjacent channel power ratio of almost ??46 dBc. In order to assess the feasibility of the proposed predistorter, it is completely implemented in the Kintex FPGA using Vivado HLS. This proposed model enables a more accurate modeling of nonlinear distortion and memory effects compared to the previous linearization methods. This paper presents the new linearization method using the genetic algorithm based Hammerstein structure.     

Implementation of a Radix-4, Parallel Turbo Decoder and Enabling the Multi-Standard Support

This paper presents a unified, radix-4 implementation of turbo decoder, covering multiple standards such as DVB, WiMAX, 3GPP-LTE and HSPA Evolution. The radix-4, parallel interleaver is the bottleneck while using the same turbo-decoding architecture for multiple standards. This paper covers the issues associated with design of radix-4 parallel interleaver to reach to flexible turbo-decoder architecture. Radix-4, parallel interleaver algorithms and their mapping on to hardware architecture is presented for multi-mode operations. The overheads associated with hardware multiplexing are found to be least significant. Other than flexibility for the turbo decoder implementation, the low silicon cost and low power aspects are also addressed by optimizing the storage scheme for branch metrics and extrinsic information. The proposed unified architecture for radix-4 turbo decoding consumes 0.65 mm² area in total in 65 nm CMOS process. With 4 SISO blocks used in parallel and 6 iterations, it can achieve a throughput up to 173.3 Mbps while consuming 570 mW power in total. It provides a good trade-off between silicon cost, power consumption and throughput with silicon efficiency of 0.005 mm²/Mbps and energy efficiency of 0.55 nJ/b/iter.     

Modeling the effect of non-linear process parameters on the prediction of hydrogen production by steam reforming of bio-oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R²) > 0.9. A low mean square error of 3.024 × 10⁻²⁴ and 6.22 × 10⁻¹⁵ for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.     

Recent Developments in Minimal Processing: A Tool to Retain Nutritional Quality of Food

The modernization during the last century resulted in urbanization coupled with modifications in lifestyles and dietary habits. In the same era, industrial developments made it easier to meet the requirements for processed foods. However, consumers are now interested in minimally processed foods owing to increase in their awareness to have fruits and vegetables with superior quality, and natural integrity with fewer additives. The food products deteriorate as a consequence of physiological aging, biochemical changes, high respiration rat,e and high ethylene production. These factors contribute substantially to discoloration, loss of firmness, development of off-flavors, acidification, and microbial spoilage. Simultaneously, food processors are using emerging approaches to process perishable commodities, along with enhanced nutritional and sensorial quality. The present review article is an effort to utilize the modern approaches to minimize the processing and deterioration. The techniques discussed in this paper include chlorination, ozonation, irradiation, photosensitization, edible coating, natural preservative use, high-pressure processing, microwave heating, ohmic heating, and hurdle technology. The consequences of these techniques on shelf-life stability, microbial safety, preservation of organoleptic and nutritional quality, and residue avoidance are the limelight of the paper. Moreover, the discussion has been made on the feasibility and operability of these techniques in modern-day processing.     

Sensitivity and specificity of a short questionnaire for food insecurity surveillance in Iran

BACKGROUND: Food insecurity is frequent in both developed and developing countries, affecting from 5% to 25% of the general population. It has considerable health impacts on the physical, social, and psychological status of individuals in communities suffering from food insecurity. OBJECTIVE: The aim of this study was to document the epidemiologic features of food insecurity in the northwest region of Iran and to evaluate the sensitivity and specificity of a short-form (six items) questionnaire for screening of food insecurity in the region. METHODS: This cross-sectional study was conducted on 300 subjects (132 male and 168 female) selected randomly in the Asadabadi area of the northwest of Iran. Information on food consumption was obtained by a 24-hour food-recall questionnaire for 3 days in a week. This information was compared with the data from the Household Food Security Scale (six-item short questionnaire) to assess the applicability of this short scale for the surveillance of food insecurity. Hunger was defined as inadequate intake of energy. Hidden hunger was defined as adequate intake of energy and inadequate intake of one (or more) of four key nutrients (protein, calcium, vitamin A, and vitamin B2). RESULTS: The prevalence of hunger and hidden hunger in the area according to the 24-hour food-recall questionnaire was 26% and 42%, respectively. Only 32% of the study population was secure in terms of having access to all key nutrients. The sensitivity, specificity, and accuracy of the short questionnaire for screening for hunger in the population were 98.7%, 85.5%, and 89%, respectively; and the corresponding values for hidden hunger were 23.5%, 96.9%, and 56.3%. CONCLUSIONS: Our findings indicate that food insecurity is prevalent in the northwest of Iran. The short questionnaire (six items) may be used as a simple, low-cost, rapid, and useful tool for the screening of food insecurity and energy intake in similar areas.     

Zinc oxide–silver/water hybrid nanofluid flow toward an off-centered rotating disk using temperature-dependent experimental-based thermal conductivity

Herein, the off-centered stagnation flow and heat transfer of zinc oxide–silver/water hybrid nanofluid over a rotating disk according to the mass-based algorithm is studied. It is assumed that the nanoparticles have a spherical shape. Also, the velocity slip between the base fluid and nanoparticles is negligible. The Prandtl number is kept constant at 6.2. In addition, it has been used an experimental relation for effective thermal conductivity which is a function of volume fraction and temperature. The governing partial differential equations are converted to dimensionless ordinary differential equation (ODE)s by the similarity transformation method. The simplified ODEs are solved numerically by the bvp4c function from MATLAB which is an efficient and reliable code according to the three-stage Lobatto IIIa formula. The influence of rotational parameters and both nanoparticles masses on the profiles and quantities of engineering interest are presented and discussed in detail. It is shown that the flow becomes complicated when there is a distance between the flow axis and the disk axis. Under determined conditions for a hybrid nanofluid with 30-g mass for both nanoparticles and 100-g mass for pure water, adding 30 g of the second nanoparticle's mass into the base fluid leads to enhance all hydrodynamic quantities of engineering interest by about 4.3%, while dispersing 30 g of the first nanoparticle's mass inside water results in decreasing the similarity temperature gradient at the surface about 3.6%. Also, when the disk rotates faster, the maximum radial velocity near the disk, s′(0) and f″(0) increases.