Performance Assessment and Optimization of Biomass Steam Turbine Power Plants by Data Envelopment Analysis

As rice husk is abundantly natural resource in Thailand, it has been used as the biomass energy resource in the stream turbine power plants, in particular to Very Small Power Producers (VSPPs). The VSPPs' plants produced by rice husk is generally found in many regions of Thailand, however its performance efficiency and optimization has never been assessed at any level. This study aimed to fulfill this gap by adopting the method of Data Envelopment Analysis (DEA) to relatively measure the performance efficiency of the decision making units (DMUs), as well as to adjust input surpluses found in order to maximize the overall efficiency scores. The secondary data recorded in 2012 were collected from the power policy bureau of Thailand and Energy for Environment Foundation, totally 47 rice husk steam turbine power plants. The empirical results showed that a CRS-DEA and VRS-DEA model performed efficiency scores at 0.874 and 0.882, respectively. The input surpluses of capacity and purchasing cost of rice husk were emphasized to increase its unit efficiency. Achieving the Thai government's aim of sustainable, renewable energy would boost up many utility plants to use rice husk for electricity generation in the nearer future. Keywords: Biomass; Data Envelopment Analysis; Stream Turbine JEL Classifications: Q420; M11

Factors Affecting the Migration of Agricultural Household Members in Northern Thailand

Agricultural household members in northern Thailand possess large areas of farm large that would seem to encourage them to settle permanently into a life of farming. However, these people have been migrating from rural areas into the urban, Bangkok metropolis and its surrounding provinces for a long time leading to increase economic risk. Migration is driven by inequality of income distribution between rural and urban areas and also the unbalanced economic growth between the country side and urban areas. The Thai government has attempted to respond to alleviate migration problems but has so far been unsuccessful. This research aims to (i) determine the characteristics of agriculturists in northern Thailand, (ii) study the amount and type of out-migration, and (iii) analyse the factors affecting the migration of Thai agricultural household members in northern Thailand. Panel data is collected by using the secondary data from both Office of Agricultural Economics and National Statistical Office in Thailand. The research areas are the northern region of Thailand covering 17 provinces. Analysis of Proportion Data is employed for analyzing the data of crop years 1995/96 to 2008/09. The results find that there are seven factors; net cash farm income, agricultural farm areas, irrigated farm areas, agricultural labor, education level of farmers, time trend, and upper northern area that can explain the migration ratio with under levels of differing significance. Most factors are consistent with presumption except for agricultural farm area factor. Only one factor, number of agricultural population, cannot explain the migration ratio because it faces the statistic problem of an insignificant result. Furthermore, the dummy variable is set into migration model for comparison migration number between the upper northern area and the lower northern area. It showed that the upper northern area has a higher migration rate than the lower area because of the former's unsuitable farm area, land utility problem, and insufficient water supply causing the farmers to migrate to urban areas in search of good job opportunities.Abstract (i) Acknowledgement (iii) Contents (iv) List of Tables (vi) List of Figures (vii) I. Introduction (1) (I-1) Introduction (1) (I-2) Objectives (5) (I-3) Expected Results (5) (I-4) Data Sources (5) (I-5) Research Scope (6) (I-6) Research Definitions (6) (I-7) Research Flowchart (8) II. Literature Reviews (9) (II-1) Overview of Migration to Cities (10) (II-2) Study on Migration Types (12) (II-3) Study on Factors Affecting the Migration (14) III. Data and Methodology (18) (III-1) Research Areas (18) (III-2) Agriculturists Characteristics (20) (III-3) Out-Migration Types (26) (III-4) Methodology (29) IV. Analysis and Empirical Results (33) V. Conclusion and Discussion (41) (V-1) conclusion (41) (V-2) Discussion (45) References (48) Appendixes (53

A study of the potential of by-products from pineapple processing in Thailand: Review article

The data, focusing on the supply of pineapple for industrial processing in Thailand during the period from 2011-2020, was acquired. The data indicated that productivity had tended to decrease during the period between 2011-2015 due to phenomena of drought and a reduction in prices, while increasing trends were observed during the years between 2016 - 2019. In the year 2020, Thailand was the biggest exporter of canned pineapple in the world, and the export value was approximately 345 million U.S. dollars. During the process, the generated by-products were peels, cores, stems, and crowns at approximately 35.5, 14.7, 4.6, and 4.3%, respectively. Based on the annual production of 1,689,884 tons, the total by-products from pineapple processing would generate 993,402.4 tons, which could be divided into peels, cores, stems, and crowns at 596,713.8, 247,089.9, 773,20.7, and 722,78.0 tons, respectively. Valorization of by-product for health applications such as pharmaceutical, cosmetic, and health food has been reviewed.&nbsp

Deep learning algorithms were used to generate photovoltaic renewable energy in saline water analysis via an oxidation process

The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is done using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient. HIGHLIGHTS This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques.; Forecasting energy demand is an essential component of PV that aids in the planning of power generation as well as energy trading with a commercial grid.; Deep learning-based models hold great promise for forecasting consumer demands and RES energy generation.

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO2 is reduced to negligible levels

Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq

Estimating the amount of solar radiation is very important in evaluating the amount of energy that can be received from the sun for the construction of solar power plants. Using machine learning tools to estimate solar energy can be a helpful method. With a high number of sunny days, Iraq has a high potential for using solar energy. This study used the Wavelet Artificial Neural Network (WANN), Wavelet Support Vector Machine (WSVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques to estimate solar energy at Wasit and Dhi Qar stations in Iraq. RMSE, EMA, R2, and IA criteria were used to evaluate the performance of the techniques and compare the results with the actual measured value. The results showed that the WANN and WSVM methods had similar results in solar energy modeling. However, the results of the WANN technique were slightly better than the WSVM technique. In Wasit and Dhi Qar stations, the value of R2 for the WANN and WSVM methods was 0.89 and 0.86, respectively. The value of R2 in the WANN and WSVM methods in Wasit and Dhi Qar stations was 0.88 and 0.87, respectively. The ANFIS technique also obtained acceptable results. However, compared to the other two techniques, the ANFIS results were lower, and the R2 value was 0.84 and 0.83 in Wasit and Dhi Qar stations, respectively

Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

Buildings account for sixty percent of the world’s total annual energy consumption; therefore, it is essential to find ways to reduce the amount of energy used in this sector. The road administration organization in Jakarta, Indonesia, utilized a questionnaire as well as the insights of industry experts to determine the most effective energy optimization parameters. It was decided to select variables such as the wall and ceiling materials, the number and type of windows, and the wall and ceiling insulation thickness. Several different modes were evaluated using the DesignBuilder software. Training the data with a supported vector machine (SVM) revealed the relationship between the inputs and the two critical outputs, namely the amount of energy consumption and CO2 production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO2 is reduced to negligible levels

Utilization of enriched hydrogen blends in the diesel engine with MgO nanoparticles for effective engine performance and emission control

The influence of hydrogen on the diesel engine has been examined in this study. In addition, the impact of MgO nanoparticles was also analysed by conducting a series of tests on samples such as Diesel (100 % diesel), DN (Diesel-50 ppm MgO), H1N (10 % Hydrogen-50 ppm MgO) and H2N (20 % Hydrogen-50 ppm MgO). Hydrogen was injected through intake manifold at the volume of 10 % and 20 %. Nanoparticles were dispersed using the ultrasonication techniques to accrue stable suspension. The experiments were conducted between 6 N-m to 24 N-m loads on a four-stroke single cylinder engine. The parameters such as brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), and heat release rate (HRR) were assessed. In addition to the performance and combustion, the environmental impact of the test blends was also analysed by examining the exhaust with a gas analyser. From the series of tests, it was evident that hydrogen enrichment in the test blends reported lower levels of emissions compared to neat diesel. The formation of the hydrocarbons (HC), nitrogen of oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2) was reduced due to the drop in the carbon atoms and enriched oxygen content in the combustion chamber. With regard to the performance, the hydrogen enriched nanoparticle blends reported peak BTE (37 %) and HRR (75 J/deg) than the other test blends. By assessing all the results, the addition of hydrogen is a potential option to reduce the environmental impact created by the fossil fuel without forfeiting the engine efficiency. © 2022 Elsevier LtdKing Saud University, KSU; Chiang Mai University, CMU: RSP-2022/23