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A successful rice production on swampland would require a planting material from high yielding varieties adaptated to the swampy ecosystem. This study was carried out to compare the growth and yield characteristics of five rice lines and a check variety as grown on non-tidal swampland. The lines were F4 generation of bulk selection from the crosses involving Bengkulu swamp rice landraces (Hanafi Putih, Tigo-tigo, Harum Curup, and Lubuk Durian) and high yielding varieties for the irrigated field (Sidenuk and Bestari). The trial was conducted on a shallow non-tidal swampland with stagnant inundation no more than 50 cm in depth often occurred during the plant life cycle. The lines and the check variety (Inpara 4) were arranged in a randomized complete block design with three replications. Observations were made for the agronomic performances of the plant, including plant height, total tiller number clump-1, effective tiller number clump-1, heading date, maturity date, panicle length, grain number panicle-1, 100-grain weight, and grain yield clump-1. Significant variation among the genotypes was found for all observed traits. On average, the evaluated lines showed comparable growth and yield performances to the check variety. Tigo-tigo × Bestari was the tallest line and potential for medium depth swampland. This line showed good overall agronomic performances and yielded relatively higher than the check variety, but delayed in attaining maturity. For shallow swampland, Hanafi Putih x Sidenuk exhibited the most potent line by having good overall agronomic and yield performances, except late in maturity. For early maturing line, Lubuk Durian x Hanafi Putih showed its potential for shallow swampland. Although this line was not the best, it showed better overall agronomic performances than the check variety. Keywords: F4 lines, growth and yield performance, rice landrace, swampland, plant maturity

Abstract The ability of the Distributed Generation (DG) to solve problems such as power system deregulation and power demand problems appropriate to its purpose, which is to inject electricity in a distributed manner at a point close to the load, causes the distributed generation to become the latest trend in electricity generation technology. Proper position of distributed generation is necessary in order to achieved maximum benefit from DG, which could be due to an incorrect allocation of DG sources to the power network would not only result in increased power losses, but could also jeopardize the operation of the system. This paper introduces an ACO-algorithm for optimal location of DGs using a real network in one of a rural area of Malaysia. The method is used to determine the effectiveness of DG by comparing the losses of power and the improvement of the voltage profile. As for the confirmation to the ACO method, another method known as brute force method is use to compare the data gain as validation purposes.

Drying experiments have been performed with potato cylinders and slices using a laboratory scale designed natural convection mixed-mode solar dryer. The drying data were fitted to eight different mathematical models to predict the drying kinetics, and the validity of these models were evaluated statistically through coefficient of determination (R(2)), root mean square error (RMSE) and reduced chi-square (χ (2)). The present investigation showed that amongst all the mathematical models studied, the Modified Page model was in good agreement with the experimental drying data for both potato cylinders and slices. A mathematical framework has been proposed to estimate the performance of the food dryer in terms of net CO2 emissions mitigation potential along with unit cost of CO2 mitigation arising because of replacement of different fossil fuels by renewable solar energy. For each fossil fuel replaced, the gross annual amount of CO2 as well as net amount of annual CO2 emissions mitigation potential considering CO2 emissions embodied in the manufacture of mixed-mode solar dryer has been estimated. The CO2 mitigation potential and amount of fossil fuels saved while drying potato samples were found to be the maximum for coal followed by light diesel oil and natural gas. It was inferred from the present study that by the year 2020, 23 % of CO2 emissions can be mitigated by the use of mixed-mode solar dryer for drying of agricultural products.

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.

© 2020 Elsevier Ltd Free cooling of buildings uses the nocturnal outdoor air as a heat sink via a ventilation process. This could be performed by storing the night coolness for use during the daytime as appropriate. Due to the latent heat capacity, phase change material (PCM) could play anessential role in the effective operation of the free cooling systems by shifting the daytime peak load to the night. However, there is a scarceness on the technology application in hot climates. This paper presents results of a parametric investigation into the application of PCMs as thermal energy storage (TES) to provide sustainable cooling to buildings in hot arid climate by making use of the night-time free cooling. The proposed TES medium comprises an arrangement of metallic modules filled with RT28HC PCM. Numerous geometrical configurations and operational parameters have been assessed. A transient CFD simulation has been employed using ANSYS Fluent software. Validation of the numerical results with experimental data has shown a good agreement. The results have demonstrated that the temperature difference between the PCM and the air, at appropriate air flow rate would have a significant impact on the performance of the system. A free cooling system based on the proposed arrangement has the potential to meet around 42% of a typical building cooling load and has the ability to save up to 67% of building cooling energy load in summer season compared to conventional air-conditioning systems in hot arid climates.

Charcoal has a large share in the Brazilian market. The production is carried out by pyrolysis of biomass at different temperatures, between 300 and 500 °C. In this study, the corn cob pyrolysis was investigated using thermogravimetric analysis (TGA) and Fourier Transform Infrared Spectroscopy (FTIR). Samples after pyrolysis were compared with raw biomass to evaluate changes in fuel characteristics. In DTG curves a reduction in the number of degradation peaks in the carbonized material was observed. The FTIR spectra allowed to identify the aromatic ring of the lignin in the charcoals structure, indicating the presence of this compound even in charcoals produced with a temperature of 500 °C. It can be concluded that the temperature of 400 °C was enough to completely degrade the hemicellulose and cellulose of the biomass, resulting the final product (charcoal) less reactive or thermally more resistant than the in natura corn cob.

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.