Burapha Science Journal

Performance of Recirculation Flow Roof in Wastewater Treatment Systems for Temperature Control Modeling in Floating Melon Greenhouses

Rattapol Suksomboon — 2025-02-18

Background and Objectives : This study on hybrid mathematical modeling aims to enhance the sustainability of agricultural practices in the Chi River basin by developing a wastewater treatment system for high-yield greenhouse cultivation. This system is expected to reduce financial risks associated with flooding and mitigate the worsening degradation of the Chi River. The researcher has designed a rooftop wastewater treatment system using mathematical modeling to regulate the temperature in floating melon greenhouses, addressing both flood-related challenges and wastewater issues. The system utilizes the sloped greenhouse roof (tilted at 20 degrees) to remove biochemical oxygen demand (BOD), ammonia (NH3), and nitrate (NO₃) from wastewater. Furthermore, mathematical models were developed to regulate greenhouse temperature, including the Plug-Flow Volume Temperature Reactor (PFVTR) and the Completely-Mixed Stirred Volume Temperature Reactor (CSVTR). These models, based on first-order and second-order reactions (n = 1 and n = 2), control temperature (T) by adjusting the recirculation ratio (QR/Qin = R) and hydraulic retention time (HRT), utilizing the ratio of cross-sectional area to longitudinal area (A_C: A_L)to determine background concentration temperature (T*) and the Coefficient of Temperature (kT) for model validation.

Methodology : The CTFMG system (Control Temperature in Floating Melon Greenhouses) measures 5.0 meters in width, 3.0 meters in height, and 6.0 meters in length with a floating melon density factor (f) of 0.5 and a total volume of 90 cubic meters. The cross-sectional to longitudinal area ratio (A_C: A_L) is less than 1:4. The system's 18-square-meter plastic-sheet rooftop is inclined at 20 degrees. Water recirculation rates were set at 3, 4, 5, 6, and 7 liters per minute, while wastewater inflow (Qin) was maintained at 1 liter per minute. The treated wastewater was analyzed for BOD, NH3, and NO3 levels. The air exchange system operated with an airflow rate (QAin & QAout) of 576 cubic meters per day, utilizing laminar flow with hydraulic retention times (HRT) of 22.33 and 44.66 days. Temperature measurements were taken during April, the hottest month, to assess the impact on melon production. The input temperature (Tin) was controlled at 36 ± 2.5°C, and the output temperature (Tout) was recorded to determine the effect of different recirculation ratios (QR/Qin = R). Data were processed using Microsoft Excel to calculate background concentration temperature (T*), first- and second-order reaction rates (n = 1 and n = 2), and the Coefficient of Temperature (kT) for both PFVTR and CSVTR models. The accuracy of the mathematical models was verified, and the feasibility of floating melon greenhouses was assessed based on Net Present Value (NPV) and Benefit-Cost Ratio (B/C Ratio), with a one-year project lifecycle.

Main Results : Results from the study on the Control Temperature in Floating Melon Greenhouses (CTFMG) utilize a roof area of approximately 18 square meters, inclined at 20 degrees from the horizontal, for wastewater treatment using plastic sheet materials. The recirculation ratio (Recirculation ratio: QR/Qin = R) tested at values of 3, 4, 5, 6, and 7 achieved removal efficiencies for BOD, NH3, NO3 (Removal BOD NH3 NO3 Efficiency) as follows: (5.36 ± 2.59%, 2.83 ± 2.85%, and 10.00 ± 0.031%), (25.07 ± 2.56%, 20.49 ± 2.83%, and 10.75 ± 0.0295%), (30.14 ± 2.52%, 22.07 ± 2.81%, and 14.25 ± 0.0275%), (59.72 ± 2.49%, 31.29 ± 2.8%, and 23.50 ± 0.027%) and (72 ± 2.45%, 34 ± 2.78%, and 32.50 ± 0.0265%). The efficiency of temperature reduction decreased by 6.81 ± 2%, 8.36 ± 2%, 9.86 ± 2%, 11.32 ± 2%, and 12.74 ± 2%, respectively. The Control Temperature in Floating Melon Greenhouses (CTFMG) system, with a cross-sectional area to longitudinal-section area ratio (A_C: A_L) (< 1:4), achieved the highest treatment efficiency at R = 7, with removal rates of 72 ± 2.45% BOD, 34 ± 2.78% NH3, and 32.50 ± 0.0265% NO3, and a maximum temperature reduction efficiency of 12.74 ± 2%. The system controlled the temperature from an inlet temperature (Temperaturein: Tin) of 36 ± 2.5°C to an outlet temperature (Temperatureout: Tout) of 31.00 ± 1.5°C. From the Plug-Flow Volume Temperature Reactor (PFVTR) and Completely-Mixed Stirred Volume Temperature Reactor (CSVTR) models, first-order and second-order reactions (n = 1 and n = 2) were observed. background concentration temperature (T*) was 15.55 ± 2.0°C. The Coefficient of Temperature (kT) values for both models were as follows: 0.008, 0.00004 (1/day) and 0.0009, 0.00006 (1/day). The coefficient of determination (R²) values were 0.8398, 0.8497, 0.9306, and 0.9526. From the economic feasibility analysis of the floating melon greenhouse for agriculture, based on Net Present Value (NPV), the system yielded 1,680 THB per square meter, with a Benefit – Cost Ratio (B/C Ratio) of 1.68 and a payback period of 1 year.

Conclusions : The Control Temperature in Floating Melon Greenhouses (CTFMG) system exhibited a second-order reaction (n = 2) in the Completely-Mixed Stirred Volume Temperature Reactor (CSVTR) model. The Coefficient of Temperature (kT) was determined to be 0.00006 1/day. In terms of economic feasibility, the floating melon greenhouse demonstrated a Net Present Value (NPV) and a Benefit-Cost Ratio (B/C Ratio) that supported its financial viability, with a payback period of 1 year.

Development and Analysis of Growing Media for Lettuce Cultivating from Used Mushroom Culture

Sutthirak Uansiri — 2025-02-18

Background and Objectives : In Chom Bueng District, Ratchaburi Province, there is a large amount of mushroom farming, especially the oyster mushroom (Pleurotus spp.) farm, which is grown using mushroom cultures. At the end of the mushroom harvest process, there will be used mushroom culture. This waste material is managed by using it as fertilizer in fields and as an ingredient for making new mushroom culture. However, this method may cause infestation from other fungi that are contaminated in the used mushroom culture. Therefore, the biological fermentation process is used to help digest mushrooms or various fungi and is a process to reduce the amount of carbon and nitrogen so as not to affect the plants. So that waste materials from mushroom cultivation have the potential to be applied in the production of plant-growing materials, which will help reduce the amount of old mushroom lumps thrown into the environment. This research, therefore, utilizes the used mushroom culture by making plant material by fermenting it with various types of organic materials. The objective is to reduce the amount of agricultural waste from mushroom farms by developing planting materials for lettuce and to analyze the physical and chemical properties of the planting material.

Methodology : Used mushroom cultures were fermented with organic materials, including cow dung, Rain Tree leaves, raw rice husks, and bamboo leaves, in various proportions, totaling 5 formulas. The results of the fermentation were studied after 40, 50, and 60 days of fermentation. The planting materials were analyzed for physical and chemical properties, including pH, organic matter content, total nitrogen content, phosphorus content, and potassium content. All planting materials were then tested for growing 15-day-old green oak lettuce and red oak lettuce, approximately 1–2 centimeters tall, in 8x16-inch planting bags. Each bag contained 1 kilogram of planting material. For each planting material, 30 plants were used. The plants were watered thoroughly and placed in an experimental greenhouse covered with a black shade cloth filtering 50% of the light. Watering was done at least 1–2 times a day. Growth measurements were performed using a simple random sampling method, selecting 3 plants per type of vegetable. The plant height and number of leaves were measured once a week until the planting period reached 6 weeks. After 6 weeks of growth, bush size, root length, and fresh weight were measured. Before the planting material was analyzed, it was sieved to remove non-degraded materials. This study was designed as a completely randomized trial. Each set of planting material was repeated 3 times. The obtained data were analyzed using a One-Way Analysis of Variance (ANOVA) with the F-test, and the means of the data were compared using Duncan's Multiple Range Test (DMRT). Microsoft Excel was used for data analysis at the p < 0.05 level. Data are presented as mean ± standard deviation (S.D.).

Main Results : The results showed that after 60 days of fermentation, all planting materials were well decomposed. The yield was 60-75% by volume, indicating that the used mushroom culture can be utilized as a mixture of planting material and to reduce the amount of the used mushroom culture into the environment. In addition, all planting material formulations showed significantly different analytical values (p < 0.05). The suitable planting material is Formula 1 (T2), which contained a mixture of used mushroom culture : cow dung : Rain Tree leaves : raw rice husk : bamboo leaves in the ratio of 1 : 1 : 1 : 1 : 1. After fermenting the planting media for 60 days in plastic baskets, the results of physical and chemical analysis were revealed as followed: the pH was 6.0±0.06, the EC was 1.6±0.10 dS/m, the OC content was 7.23±0.01%, OM was 12.47±0.01%, total nitrogen was 1.08±0.06%, total phosphorus was 0.22±0.03% and total potassium was 0.57±0.03%.

Conclusions : This research has used agricultural waste from mushroom farms to develop into planting materials for green oak lettuce and red oak lettuce, which are vegetables with high consumption. The used mushroom cultures were fermented with organic materials in different ratios, totaling 5 formulas. After 60 days of fermentation, the results of the study found that all planting materials decompose well and yield 60-75 percent by volume. The most suitable planting material is the T2 formula, which is a mixture of leftover mushroom spawns: cow dung: crab claw leaves: raw rice husks: bamboo leaves in a ratio of 1: 1: 1: 1: 1. In addition, the study of the physical and chemical properties of this formula of planting materials found that it has a higher amount of nutrients that can support plant growth than other formulas. When the T2 formula of planting materials was used to grow lettuce, it was found that the plants grew better overall than the other formulas. The plants could also be harvested within 40 days without the need for additional fertilizers.

Analysis of Spatial and Temporal Patterns of Meteorological Drought Exposure and Its Impact on Economic Crops, Nakhon Ratchasima Province, Thailand

Suwit Ongsomwang — 2025-02-19

Background and Objectives: Drought is one of the most complex influencing factors among all-natural disasters. It is a complex phenomenon because of the unpredictable start and end of its period, the length of the event, as well as the nonspecific spatial extent or geography and uncertain frequency and intensity. Meanwhile, meteorological drought is usually defined based on the degree of dryness and the duration of the dry period. Nakhon Ratchasima province is a drought-prone area since the annual rainfall between 1975 and 2022 was mostly lower than the average annual rainfall in the same period, with a value of 1,223.59 mm for about 24 years. Therefore, this study aims to examine spatial and temporal patterns of meteorological drought exposure and its impact on economic crops in Nakhon Ratchasima province. The objectives of the study were (1) to classify and map meteorological drought frequency, intensity and exposure and (2) to analyze spatial and temporal patterns of meteorological drought exposure and its impact on economic crops. Herein

Methodology: The research methodology comprised four main steps after the Standardized Precipitation Index calculation in 4 periods, including 3m7 (May to July), 3m10 (August to October), 6m10 (May to October), and 12m (January to December): (1) meteorological drought frequency index extraction and classification, (2) meteorological drought intensity index extraction and classification, (3) meteorological drought exposure index extraction and classification, and (4) spatial and temporal patterns analysis of meteorological drought exposure and its impact on economic crops: rice, cassava, sugarcane and corn. Herein, three meteorological drought indices, meteorological drought frequency, meteorological drought intensity, and meteorological drought exposure, were calculated based on a long-term rainfall record (1975-2022) from 37 stations. In the meantime, spatial and temporal patterns of meteorological drought exposure at district and sub-district levels using zonal analysis with majority operation.

Main Results: The most dominant class of meteorological drought exposure classification of the 4 periods (3m7, 3m10, 6m10 and 12m) was a moderate, moderate, moderate, and low covered area of about 33.22%, 35.26%, 42.11% and 35.69%, respectively. The spatial distribution of the meteorological drought exposure classification of the 4 periods displayed a completely different pattern. Still, the meteorological drought exposure severity classification of the 4 periods showed a strong positive linear relationship among them. The correlation coefficient values varied from 0.8100 to 0.8966. These results imply the similarity of meteorological drought exposure patterns among 4 periods. Besides, the majority severity classification of the meteorological drought exposure in the 6m10 period exhibited the highest impacts at district and sub-district levels, with 16 districts and 138 sub-districts. Based on the spatial pattern changes of meteorological drought exposure severity levels among 3-periods (3m7, 3m10 and 6m10), covering the economic crop calendar, the severity classification of the meteorological drought exposure in the 6m10 period exposed the highest meteorological drought compared with other periods (3m7 and 3m10). In the meantime, the potential impact areas of meteorological drought exposure in the 6m10 period (May to October) at moderate, high, and very high severity levels on rice in 2023 was about 3,939.40 sq. km 64.65% of the total area of rice, cassava about 2,918.67 sq. km 75.74% of the total area of cassava, sugarcane, about 1,423.47 sq. km or 69.48% of the total area of sugarcane, and corn, about 441.33 sq. km or 56.38% of the total area of corn. Furthermore, based on Pearson bivariate correlation analysis, the most dominant meteorological drought exposure index that impacts crop yield is the meteorological drought exposure index in the 3m7 period (May to July). This index displayed a negative linear relationship with the average rice, cassava and corn yield between 2011 and 2022. On the contrary, the meteorological drought exposure index showed no linear relationship with sugarcane since a multi-cropping system of about three years is applied for sugarcane by farmers.

Conclusions: Spatial and temporal patterns analysis of meteorological drought exposure were successfully conducted based on a standardized precipitation index for quantifying the severity of drought and its impact on economic crops in different periods (3m7, 3m10, 6m10 and 12m). The presented research workflow can be used as a guideline for the relevant government agencies, such as the Department of Agricultural Extension and the Department of Disaster Prevention and Mitigation, to monitor meteorological drought for mitigation of the potential impact of drought on economic crops in the future. In addition, early warning systems of meteorological drought at the regional level are recommended to be implemented by the Thai Meteorological Department.

Qualities of Mixed Vegetables/Fruit Juice from Fresh-Cut Salad Waste under High-Pressure Processing

Nattavong Fuangpaiboon — 2025-02-20

Background and Objectives : High pressure processing (HPP) is a non-thermal food processing technology that has emerged as innovative methods capable of microbial inactivation and enzyme inhibition while preserving nutritional value, flavor compounds, and bioactive substances. HHP is a favorable application in food industry including juices, jams, and other fruit products. The core benefits of HHP processing are the reduction or significant elimination of heating, avoiding the degradation of the food component from heat. In addition, there is a large amount of fresh-cut salad waste after the cutting process that cannot be sold due to their unappetizing characteristics but have nutrition values and bioactive substances such as antioxidants, etc., with no difference from fresh salad vegetables. Therefore, food processing is required to change this appearance from food waste to juice but still preserve the nutrient value of the bioactive compounds as much as possible. This study aimed to investigate the effects of high-pressure processing (HPP) at various levels of pressure and time on the physical properties, chemical properties, and microbials of a mixed vegetable and fruit juice product.

Methodology : Three types of vegetables (such as Green oak vegetables, red oak vegetables, and mini cos lettuce) from fresh-cut salad waste) and fruits, including red grapes, red apples, and lemons, were extracted using a cold press processor and then mixed. The proportion of mixed vegetables (Green oak vegetables, red oak vegetables, and mini cos lettuce in the proportion 45:45:10 % by weight, respective), red grapes, red apples, and lemons were 59.58, 23.57, 14.85 and 2 % by weight, respectively. Mixed vegetables and fruit juice were packed in polyethylene terephthalate bottle (PET) for 150 ml. Then, mixed vegetables/fruit juice were examined as pressure levels of 300, 400, and 500 MPa applied for durations of 3, 6, and 9 minutes. All Mixed vegetables and fruit juice samples were physical, chemical and microbiology analyzed such as color, turbidity, total soluble solids content, pH value, total phenolic contents, antioxidant activity by DPPH and FRAP assays, total microbial and yeast and mold contents.

Main Results : Results demonstrated that pressure and time had significant interactive effects on redness (a*) and total microbial count (p≤0.05). Furthermore, pressure levels significantly influenced brightness (L*), redness (a*), yellowness (b*), pH, and antioxidant activity as measured by the Ferric Reducing Antioxidant Power (FRAP) assay (p≤0.05). Mixed vegetables and fruit juice under pressure at 500 MPa for 5 min was increased in a* value (a* = -0.50, p≤0.05). However, pressure levels and time did not affect turbidity and total soluble solids content (TSS) of mixed vegetables and fruit juice samples (p>0.05). Notably, increasing pressure levels correlated with decreased FRAP antioxidant activity (p≤0.05). FRAP contents of mixed vegetables and fruit juice under pressure at 500 MPa with duration between 3 and 9 min ranged from 7.45 to 7.93 µg TE/100 g sample. Pressure levels, times and interaction between pressure levels and time (P*T) did not affect total phenolic contents and antioxidant activity by DPPH assays of mixed vegetables and fruit juice (p>0.05). Total phenolic contents and antioxidant activity by DPPH assays of mixed vegetables and fruit juice ranged from 35.94-41.21 µg GAE/100 g samples and 4.26-4.56 µg TE/100 g samples, respectively. Processing time significantly affected only brightness and pH (p≤0.05). The pH values of mixed vegetables and fruit juice samples under HPP process at 300 MPa for 3-6 min were ranged between 3.83 and 3.84. When pressure level and time increased, total plate count decreased (p≤0.05). Total microbial contents of mixed vegetables and fruit juices under pressure level more than 300 MPa for 3-9 min had ranged from 3.33 to 6.00 CFU/ml. While pressure, time, and their interaction did not significantly impact yeast and mold counts in the juice samples (p>0.05). The results exhibited that total yeast and molds of mixed vegetables and fruit juices under HPP were lower than mixed vegetables and fruit juices with untreated HPP. All treated samples exhibited microbial, yeast, and mold counts below the general criteria and limits set for high-pressure pasteurized acidic products (pH ≤ 4.64). These findings align with the Thai Food and Drug Administration’s (2019) guidelines, which recommend pressures of at least 400 MPa for 1-20 minutes for such products.

Conclusions : High-pressure processing of mixed vegetable and fruit juice at 400 MPa for 6 minutes emerges as a promising non-thermal treatment with potential for industrial application. This HPP condition exhibits a higher of total phenolic contents, and antioxidant activity by DPPH and FRAP assays in mixed vegetables and fruit juices from fresh-cut salad waste. Moreover, microbial, yeast, and mold counts align with the Food and Drug Administration’s (2019) guidelines with an appropriate cost. This method not only ensures microbiological safety and quality but also presents an opportunity to add value to by-products from the fresh-cut produce industry or substandard vegetables, transforming them into nutritious health beverages.

Application of Google Earth Engine for Monitoring Mangrove Forest Changes in Satun Province

Jirawat Jantongpoon — 2025-02-20

Background and Objectives : Satun Province has rich and ecologically important mangrove forests that play a vital role in both the ecosystem and the economy. However, a continuous decline in these mangrove forests has been observed over recent years. Challenges within the study area include limited human resources, which impede effective monitoring and management. Furthermore, conventional remote sensing techniques necessitate specialized hardware, software, and data processing capabilities, thereby imposing limitations on accessibility and efficiency for certain organizations. This study aims to propose a methodology for utilizing Google Earth Engine (GEE), a powerful cloud-based platform for online satellite imagery analysis, to monitor changes in mangrove forest areas in Satun Province, covering the districts of Thung Wa, La Ngu, Tha Phae, and Mueang Satun. This study, covering the six-year period from 2018 to 2023, pursues several key objectives: to mitigate conflicts arising from human encroachment on mangrove ecosystems, to minimize hardware and software costs associated with traditional monitoring methods, and to address the critical issue of mangrove forest decline attributed to persistent encroachment and land use change.

Methodology : This study employed Sentinel-2 Multi Spectral Instrument (MSI) Level-1C and Level-2A imagery acquired during periods of minimal cloud cover over the study area within the specified year. Cloud masking procedures were implemented to mitigate cloud contamination, and vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), were computed to generate training data for supervised land cover classification. Supervised classification was performed using the Random Forest algorithm, a robust machine learning classifier, with 30 iterations, implemented within the Earth Engine Code Editor on the Google Earth Engine (GEE) platform. Classification accuracy was assessed through field surveys and comparison with reference data derived from Google Earth. Furthermore, land use and land cover (LULC) change analysis was conducted for the period spanning 2018 to 2023 using Geographic Information Systems (GIS). Change detection analysis was performed by overlaying the annual classified datasets to identify areas of both persistence and change. The resulting land cover was categorized into six key classes relevant to the study area: natural water bodies (W1), mangrove forests (F3), aquaculture farms (A9), built-up areas (U2), agricultural areas (A0), and miscellaneous areas (M4). LULC changes were then summarized in a time series format to quantify and visualize the dynamics of land cover change.

Main Results : The integration of Google Earth Engine (GEE) with the Random Forest (RF) algorithm, a powerful machine learning classifier, significantly enhances the efficiency of mangrove forest change detection. Sentinel-2 satellite imagery, accessed through the Earth Engine Data Catalog, was processed using cloud computing resources, facilitating rapid and efficient analysis of extensive datasets. The land use and land cover classification achieved an overall accuracy exceeding 80% and a Kappa coefficient ranging from 0.6 to 0.8, based on 256 independent validation points strategically distributed across the study area. This validation, grounded in binomial probability theory, indicates an acceptable level of reliability for the classification results. The resulting data offer significant potential for effective mangrove resource management. The land use and land cover classification results showed the proportional distribution of land cover types in the study area. In 2018, mangrove forests constituted the largest proportion, covering 73.406% of the total area. This was followed by agricultural areas, which occupied 12.360%, natural water bodies at 1.803%, aquaculture areas at 7.278%, built-up areas at 0.873%, and finally, miscellaneous areas at 4.280%. By 2020, a notable shift in land cover was observed, with mangrove forest cover experiencing a decrease to 71.033%. This decrease was subsequently followed by a recovery, as mangrove forest cover increased to 72.300% by 2023. Analyzing the changes between 2018 and 2020 reveals a net loss of 2.373% in mangrove forest area. This loss was primarily attributed to the conversion of mangrove forest land into agricultural land, indicating a shift in land use practices within the study area. Conversely, the period from 2020 to 2023 witnessed a reversal of this trend, with mangrove forest area exhibiting a net increase of 1.267%. This increase can be primarily explained by the reconversion of agricultural land back into mangrove forests, suggesting a potential recovery or restoration of mangrove ecosystems.

Conclusions : The Google Earth Engine (GEE) platform facilitates the rapid online processing of satellite imagery, mitigating hardware limitations. However, the processing of very large datasets may necessitate segmentation into smaller tiles. For applications in other geographic regions, the definition of context-specific parameters and vegetation indices is crucial for achieving acceptable accuracy in land use and land cover classification. The findings of this study demonstrate the efficacy of GEE for monitoring changes in mangrove forest area over a six-year period. The observed expansion of mangrove forests between 2020 and 2023 can be attributed to the implementation of stricter governmental measures to prevent encroachment. This study represents a significant advancement in the application of satellite technology and geographic information systems (GIS) for mangrove forest conservation, offering a more cost-effective and time-efficient alternative to traditional field-based methods and enabling more efficient resource management. Furthermore, the developed methodology holds potential for future applications, such as estimating the carbon sequestration capacity of mangrove ecosystems.