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Microalgae have potential as biomass energy sources with higher photosynthetic efficiency compared to terrestrial plants. The use of polyculture systems such as native microalgae communities for microalgae cultivation has several advantages, as well as challenges due to indeterminate species composition and growth rate variation between species. This paper presents an artificial neural network (ANN) model to estimate the growth of polyculture microalgae in a semi-continuous open raceway pond (ORP). The model was comprised of a multilayer backpropagation neural network with eight input parameters, one hidden layer, and one output parameter. The model was developed using datasets collected from the cultivation of polyculture microalgae in Minamisoma City, Fukushima Prefecture, Japan. The input parameters are as follows: initial algal concentration, harvesting period (between two and three days after the growth have begun), hydraulic retention time, addition of sodium acetate, average solar radiation (μmole m−2 s−1), average temperature (oC), pH condition, and nitrate ion ( NO 3 − ) concentration. The output variable is the microalgae concentration observed during the cultivation period. The output is represented using a single neuron. The result of the study showed that the designed three-layer ANN achieved a high prediction accuracy (R2 = 0.93) for all combinations of inputs. Highlights • ANN model was proposed to estimate the growth of polyculture microalgae. • Developed using 553 datasets collected from semi-continuous Open Raceway Pond. • ANN model was able to estimate the growth of polyculture microalgae. • Model achieved R2 more than 0.9 for all input variation. • Key variables to estimate microalgal growth were solar irradiance and temperature. [ABSTRACT FROM AUTHOR]

The environmental load of continuous bioenergy production from palm oil ( Elaeis guineensis ) included with a proposed 10 ha of microalgae production system were assessed to be implemented in Indonesia. Material and energy balances, greenhouse gas (GHG) emission, nutrient requirement and also water scarcity during bioenergy production cycle were evaluated. The integrated system was developed for 60 tons h −1 of fresh fruit bunch (FFB) processing capacity of a conventional mill. Aggregate of energy-profit ratio from the proposed system was 5.20, which indicates a positive balance. The total water footprint for each palm oil and microalgae cultivation was 3.18 and 2.85 m 3  kg −1 of biodiesel production, respectively. Microalgae mix-culture has the potential to treat organic compounds from palm oil mill effluent (POME) and combined with flue gases from biomass and biogas power plant as the alternative nutrient sources contributed to net-reduction of GHG emission for 158.8 tons ha −1 of microalgae culture, annually. The integrated system produced 26,471 tons of biodiesel that included 223 tons from microalgae and contribute to 39.90% of total GHG emission reduction from diesel fuel substitute. Additional co-product of 520.33 tons year −1 of animal feed from defatted biomass also possible to be produced and have potential for environmental benefits. [ABSTRACT FROM AUTHOR]

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions. [ABSTRACT FROM AUTHOR]

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates. [ABSTRACT FROM AUTHOR]

[Display omitted] • High N and P nutrients in ADAE are suitable to be treated by microalgae consortium. • As much as 2 kg of animal feed was produced from treatment of 1 m3/d of ADAE. • The bio-oil was produced at 36–180 L/d for microalgae consortium grown in ADAE. • The integrated process produced 247–1,217 MWh y-1 of electricity from biogas. • Moreover, the process can reduce GHG emission by 2 kg-CO 2 eq kg−1 of HSCW. Anaerobic digestate animal effluent (ADAE) contains high N and P nutrients which need to be treated. In this study, an integrated process was proposed using a microalgae consortium of Chlorella and Scenedesmus. The system was designed for 71 m3/d (medium-sized) and 355 m3/d (large-sized) animals of ADAE. Process simulation estimated to produce 83–417 kg d-1 of microalgae biomass which can be used as further products. As much as 2 kg of animal feed and 36–180 L/d of bio-oil can be produced during the treatment of 1 m3/d of ADAE. The produced biogas can generate 247–1,217 MWh y-1 of electricity. Likewise, the process can reduce greenhouse gas emissions by 2 kg-CO 2 eq kg−1 of hot standard carcass weight (HSCW). This integrated system offers merits in treating ADAE as well as producing chemicals and energy with low environmental burdens. [ABSTRACT FROM AUTHOR]

Land evaluation is important for assessing environmental limitations that inhibit higher yield and productivity in tea. The aim of this research was to determine the suitable lands for sustainable tea production in the northeastern part of Bangladesh using phenological datasets from remote sensing, geospatial datasets of soil–plant biophysical properties, and expert opinions. Sentinel-2 satellite images were processed to obtain layers for land use and land cover (LULC) as well as the normalized difference vegetation index (NDVI). Data from the Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. Other vector and raster layers of edaphic, climatic parameters, and vegetation indices were processed in ArcGIS 10.7.1® software. Finally, suitability classes were determined using weighted overlay of spatial analysis based on reclassified raster layers of all parameters along with the results from multicriteria analysis. The results of the study showed that only 41,460 hectares of land (3.37% of the total land) were in the highly suitable category. The proportions of moderately suitable, marginally suitable, and not suitable land categories for tea cultivation in the Sylhet Division were 9.01%, 49.87%, and 37.75%, respectively. Thirty-one tea estates were located in highly suitable areas, 79 in moderately suitable areas, 24 in marginally suitable areas, and only one in a not suitable area. Yield estimation was performed with the NDVI (R2 = 0.69, 0.66, and 0.67) and the LAI (R2 = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively. This research suggests that satellite remote sensing and GIS application with the analytical hierarchy process (AHP) could be used by agricultural land use planners and land policy makers to select suitable lands for increasing tea production. [ABSTRACT FROM AUTHOR]

A comparative evaluation of energy requirement and CO2 emission was performed for native polyculture microalgae oil production in a wastewater treatment plant (WWTP). The wastewater provided nutrients for algae growth. Datasets of microalgae oil production and their details were collected from the Minamisoma pilot plant. Environmental impact estimation from direct energy and material balance was analyzed using SimaPro® v8.0.4. in two scenarios: existing and algal scenarios. In the existing scenario, CO2 emission sources were from wastewater treatment, sludge treatment, and import of crude oil. In the algal scenario, CO2 emission with microalgae production was considered using wastewater treatment, CO2 absorption from growing algae, and hydrothermal liquefaction (HTL) for extraction, along with the exclusion of exhausted CO2 emission for growing algae and use of discharged heat for HTL. In these two scenarios, 1 m3 of wastewater was treated, and 2.17 MJ higher heating value (HHV) output was obtained. Consequently, 2.76 kg-CO2 eq/m3-wastewater in the existing scenario and 1.59 kg-CO2 eq/m3-wastewater in the algal scenario were calculated. In the HTL process, 21.5 MJ/m3-wastewater of the discharged heat energy was required in the algal scenario. Hence, the efficiency of the biocrude production system will surpass those of the WWTP and imported crude oil. [ABSTRACT FROM AUTHOR]

• A land suitability analysis (LSA) model was developed to find suitable areas for cassava production. • A fuzzy expert system was used with a multicriteria decision for LSA. • Yield prediction method was developed using satellite remote sensing-based vegetation datasets. • NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the yield prediction model. • Ground reference validation was incorporated for yield prediction according to land suitability. Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the purpose of this research was to develop a yield prediction model based on suitable areas that assess with land suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava production was built using the fuzzy membership function. The results showed that 42.17% of the land area was highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed using the vegetation indices based on Sentinel-2 datasets with 10 m resolution for the accuracy assessment. The vegetation indices were used to predict cassava growth, biophysical condition, and phenology over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting cassava growth. The generated models were validated using regression analysis between observed and predicted yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2 = 0.62) compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2 = 0.70) than other biophysical properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest accuracy (R2 = 0.77). The ground truth data were used for the evaluation of satellite remote sensing data in the comparison between the observed and predicted yields. This developed integrated model could be implemented for the management of land allocation and yield assessment in cassava production to ensure regional food security in Indonesia. [ABSTRACT FROM AUTHOR]

This article presents an engineering study of an integrated system to produce bio-oil from microalgae biomass. The analysis is based on a pilot plant located at Minami-soma Fukushima, Japan, which further simulates 1 ha based-cultivation. Municipal wastewater and flue gases were utilized as nutrient sources for the microalgae culture of the proposed design. A flow sheet diagram of the integrated plant was synthesized by process engineering software to allow simulation of a continuous system. The design and sizing of the process equipment were performed to obtain a realistic estimation of possible production cost. The results demonstrated that nutrient savings was achieved by wastewater and CO2 utilization to the polyculture of native microalgae. Process simulation gave an estimated CO2 sequestration of 82.77 to 140.58 tons ha−1year−1 with 63 to 107 tons ha−1year−1 of potential biomass production. The integrated process significantly improved the energy balance and economics of biofuel production and also the wastewater treatment plant (WWTP). The economic analysis confirmed that higher biomass production and technology improvement were required to achieve operational feasibility and profitability of the current microalgae-based bio-oil production. [ABSTRACT FROM AUTHOR]

This study investigates the potential of microalgae oil production as an alternative renewable energy source, in a pilot project located at Minamisoma City in the Fukushima Prefecture of Japan. The algal communities used in this research were the locally mixed species, which were mainly composed of Desmodesmus collected from the Minamisoma pilot project. The microalgae oil-production processes in Minamisoma consisted of three stages: cultivation, dewatering, and extraction. The estimated theoretical input-energy requirement for extracting oil was 137.25 MJ to process 50 m3 of microalgae, which was divided into cultivation 15.40 MJ, centrifuge 13.39 MJ, drum filter 14.17 MJ, and hydrothermal liquefaction (HTL) 94.29 MJ. The energy profit ratio (EPR) was 1.41. The total energy requirement was highest in the HTL process (68%) followed by cultivation (11%) and the drum filter (10%). The EPR value increased along with the yield in the cultivation process. Using HTL, the microalgae biomass could be converted to bio-crude oil to increase the oil yield in the extraction process. Therefore, in the long run, the HTL process could help lower production costs, due to the lack of chemical additions, for extracting oil in the downstream estimation of the energy requirements for microalgae oil production. [ABSTRACT FROM AUTHOR]

Rice husk is the main by-product of the postharvest stage in rice production, which causes environmental impacts due to improper management as a solid waste. However, potential economic applications of rice husk combustion have been identified for energy generation and amorphous silica production in several industries. To minimize hazardous gaseous emissions and crystalline silica availability, rice husk combustion conditions should be properly controlled which also effect for efficient heat production. This study was conducted under different conditions of temperature, airflow, combustion time, and bulk density of rice husk in the combustion process using an experimental prototype hybrid rice husk burner with a fluidized bed. The availability of crystalline silica in rice husk charcoal and the CO and O2 compositions in the exhaust gas were analyzed using XRD analysis and gas analysis, respectively. Furthermore, elemental and thermogravimetric analyses were conducted to find the most efficient combustion parameter for the optimum conditions of rice husk combustion using the experimental rice husk burner. Therefore, the most efficient heat generation was achieved with the observation of the lowest CO emission, the nonavailability of crystalline silica in rice husk charcoal, at a low temperature and air flow rate (430 °C; 0.8 ms−1), high bulk density (175 kgm−3 and 225 kgm−3) and short combustion time (30 s). [ABSTRACT FROM AUTHOR]

A simple and rapid method for the determination of free fatty acid (FFA) content in brown rice using Fourier transform infrared spectroscopy (FTIR) in conjunction with second-derivative treatment was proposed. Ground brown rice (10 g) was soaked in toluene (20 mL) for 30 min, and the filtrate of the extract was placed in a 1 mm CaF 2 liquid cell. The transmittance spectrum of the filtrate was recorded using toluene for the background spectrum. The absorption band due to the C O stretching mode of FFAs was detected at 1710 cm −1 , and the Savitzky–Golay second-derivative treatment was performed for band separation. A single linear regression model for FFA was developed using the 1710 cm −1 band in the second-derivative spectra of oleic acid in toluene (0.25–2.50 g L −1 ), and the model displayed high prediction accuracy with a determination coefficient of 0.998 and a root mean square error of 0.03 g L −1 . [ABSTRACT FROM AUTHOR]

• A deep learning stereo vision guidance system was developed for use with speed sprayers. • A real-time deep learning machine vision algorithm, using EfficientDet or YOLOv7 for target detection, was employed. • The developed system enhances positioning in areas where GNSS may be less effective, offering effectiveness in challenging environments. • An RGB-D collision avoidance system was introduced, offering practical impact in improving operational safety. In orchards, radio frequency scintillation caused by high vegetation density can hinder the effectiveness of machinery auto guidance based on the Global Navigation Satellite System (GNSS). Signal interference not only leads to poor quality of machine operation but can also pose operational risks. In this work, we propose an alternative trajectory positioning system to supplement traditional GNSS-based navigation for machinery used in orchards. The aim of this research was to develop a deep learning machine stereo vision guidance system onboard an orchard speed sprayer. The developed system combines a collision avoidance methodology along with deep learning-driven machine vision for interrow positioning and a dead reckoning set of rules for alternating U-turns. The developed methodology was tested in 4 rows of an artificial orchard. The results show that it is possible for the embedded EfficientDet target detection algorithm to guide the equipment at 1, 1.5 and 2 km × h−1 with minimum average root-mean-square errors (RMSEs) of 0.24 m, 0.20 m, and 0.31 m, respectively. When the system navigation was performed using YOLOv7, the minimum average RMSEs for each row were 0.40 m, 0.48 m, and 0.43 m, respectively, for the abovementioned speeds. The U-turn by dead reckoning showed minimum average RMSE values of 0.56 m, 0.22 m, and 0.35 m for row navigation based on EfficientDet at 1, 1.5 and 2 km × h−1, respectively. For YOLOv7-based navigation in rows, the minimum average RMSE values at these speeds were 0.35 m, 0.81 m, and 0.44 m, respectively. This study contributes to the field by proposing an alternative navigation system for orchard machines that operates without the limitations associated with GNSS. In addition, our proposed guidance methodology introduces an RGB-D collision avoidance system with a demonstrated safety capacity for navigation under real scenario conditions. [ABSTRACT FROM AUTHOR]

• A deep learning stereo vision guidance system was developed for use with speed sprayers. • A real-time deep learning machine vision algorithm, using EfficientDet or YOLOv7 for target detection, was employed. • The developed system enhances positioning in areas where GNSS may be less effective, offering effectiveness in challenging environments. • An RGB-D collision avoidance system was introduced, offering practical impact in improving operational safety. In orchards, radio frequency scintillation caused by high vegetation density can hinder the effectiveness of machinery auto guidance based on the Global Navigation Satellite System (GNSS). Signal interference not only leads to poor quality of machine operation but can also pose operational risks. In this work, we propose an alternative trajectory positioning system to supplement traditional GNSS-based navigation for machinery used in orchards. The aim of this research was to develop a deep learning machine stereo vision guidance system onboard an orchard speed sprayer. The developed system combines a collision avoidance methodology along with deep learning-driven machine vision for interrow positioning and a dead reckoning set of rules for alternating U-turns. The developed methodology was tested in 4 rows of an artificial orchard. The results show that it is possible for the embedded EfficientDet target detection algorithm to guide the equipment at 1, 1.5 and 2 km × h−1 with minimum average root-mean-square errors (RMSEs) of 0.24 m, 0.20 m, and 0.31 m, respectively. When the system navigation was performed using YOLOv7, the minimum average RMSEs for each row were 0.40 m, 0.48 m, and 0.43 m, respectively, for the abovementioned speeds. The U-turn by dead reckoning showed minimum average RMSE values of 0.56 m, 0.22 m, and 0.35 m for row navigation based on EfficientDet at 1, 1.5 and 2 km × h−1, respectively. For YOLOv7-based navigation in rows, the minimum average RMSE values at these speeds were 0.35 m, 0.81 m, and 0.44 m, respectively. This study contributes to the field by proposing an alternative navigation system for orchard machines that operates without the limitations associated with GNSS. In addition, our proposed guidance methodology introduces an RGB-D collision avoidance system with a demonstrated safety capacity for navigation under real scenario conditions. [ABSTRACT FROM AUTHOR]

Coelastrella microalgae have gained attention due to their robustness, high lipid content, effective bioremediation agent, and ability to thrive in diverse environmental conditions. The objective of this study was to evaluate the economic feasibility of an integrated biorefinery of the unique original Coelastrella striolata var. multistriata strain 047 applied in agro-industrial wastewater, flue gases, and excess energy on a 1.2 ha open raceway pond (ORP). This study consists of three scenarios: (1) biocrude only (BO); (2) biocrude and biogas (BB); and (3) biocrude, biogas, and fertilizer (BBF). The findings indicated that the BB and BBF scenarios enhanced the financial feasibility and potential profitability of investment. This is reflected in the economic feasibility indicators for the BB scenario over a project lifetime of 20 years, which include a net present value (NPV) of $4,757,082, an internal rate of return (IRR) of 25.6%, a profitability index (PI) of 2.77, and a payback period of 3.75 years. Similarly, in the BBF scenario, financial viability is demonstrated with an NPV of $4,714,418, an IRR of 25.2%, a PI of 2.73, and a payback period of 3.79 years. Operational expenditures were identified as the most sensitive aspect of this study, particularly in the hydrothermal liquefaction (HTL) process for biocrude production. The utilization of effluent and excess energy from palm oil mill (POM) reduced the operational costs for energy and nutrients by up to 69% compared to the baseline, where palm oil mill effluent (POME) and excess energy were not utilized. Additionally, to achieve optimal results in the BO scenario, increasing the concentration of Coelastrella striolata var. multistriata strain 047 in POME proved to be the most effective approach for increasing biocrude oil production, reducing costs, and increasing economic sustainability. [ABSTRACT FROM AUTHOR]

[Display omitted] • Reducing global warming through cofiring involves supply flow analysis to gradually replace fossil by oil palm fibre. • Supply flow is modelled using linear programming for oil palm mill, pellet plant, and power plant. • At a 1% cofiring rate, 6 of 7 pellet plants are needed to reduce 1.57% impact compared to single firing of coal. • Ranges analysis implies the tolerable emission of 6 pellet plants for supply stability. • Economic consideration reveals 3 of 7 pellet plants can be constructed with slight impact changes. This study addresses the supply flow optimization to reduce global warming in a cofiring system, using the example of oil palm empty fruit bunches converted into pellets for use in power plants in Indonesia. Four oil palm mills, seven pellet plants and five coal power plants in the West Java Region were considered in this study to select the optimal supply chain. The study was performed using a life cycle assessment (LCA) for evaluating impact of single firing of coal and linear programming (LP) for optimizing the impact reduction of cofiring of coal and pellets. Our study indicates that the optimal environmental reduction can be achieved by constructing six of the seven pellet plants, which can substitute up to 1 % of the coal supply in five power plants. Accordingly, the emissions could be decreased by an average of 0.018 kg CO 2 eq/kWh for the power plants, which is equal to 0.2 Mt CO 2 eq/y or a 1.57 % reduction per year. By varying the emission factor of the pellet plants, our analysis showed a tolerable range in which the supply would not be affected. The sensitivity analysis at a 1 % cofiring rate showed that the increase in biomass supply from the oil palm mills had the highest reduction, whereas the reduction in transportation emissions provided much less impact. By adjusting the cofiring rate, the impact would not change after the 3 % cofiring rate because of the limited availability of Empty Fruit Bunch (EFB) feedstock. However, if sufficient supply could be provided, the environmental impact would be further reduced significantly. Furthermore, the scenario expansion indicated that only three pellet plants would be required for the minimum economic scale of production, although the environmental impact increased by 0.47 % compared to using six pellet plants. Therefore, this study can be used to determine the optimal supply chain scenarios by constructing an appropriate number of economically feasible pellet plants according to the biomass demand and cofiring rate of the power plant. [ABSTRACT FROM AUTHOR]

Most studies on honeycomb catalysts have been conducted using simulation models and exhaust experiments from automobiles. Very few monolithic catalyst studies have been applied to the agricultural sector, especially the catalyst exhaust system for flue purification from the biomass industry. The importance of exhaust gas purification and particulate removal from biomass power plants has become critical for evaluating the performance and environmental sustainability of biomass combustion. This is one of the first studies to investigate the performance of honeycomb catalysts for the oxidation of flue (PM2.5), (CO), and (SO2) from a rice husk briquette combustion system. The experimental setup comprised a fixed-bed electric furnace, the catalyst, an aerosol sampler, and a flue gas analyzer. Rice husk (0.1 g/mL density) and rice husk briquettes (0.8 g/mL density), were burned at 600–1000 °C for 3 min. From the results, the catalyst CO conversion rate was 100% at the optimum heated temperatures of 427.4–490.3 °C. At these temperatures, the inhibition effect of the chemisorbed CO was significantly minimized, enhancing the adsorption of oxygen, which reacted with CO to form CO2. However, SO2 oxidation was lower than that of CO because platinum-based catalysts are generally more attracted to CO in the presence of oxygen. The emission of PM2.5 decreased from its uncatalyzed-value (1169.9 mg/m3 and 1572.2 mg/m3) to its catalyzed values (18.9 mg/m3 and 170.1 mg/m3). This is a significant result in ensuring cleaner production of energy from rice husk. [ABSTRACT FROM AUTHOR]

This study offers an overview of how changing habits in consuming a cup of tea can contribute to make better environment. As the initial existing scenario, survey for picturing Indonesian consumers in preparing their cup of tea from dried leaves was conducted to urban and suburban citizens. According to the survey, both respondent groups were using LPG as the first choice in boiling water for preparing tea, followed by using an electric dispenser as the second choice. This habit causes CO2 emission from processing a cup of tea by Indonesian consumer was 24 g CO2-eq per cup of tea, excluding the tea organic waste. The portion of CO2 emission from boiling water in tea preparation was 41.93% of whole CO2 emission from plantation to served cup. The emission can be significantly reduced by converting dried tea (initial scenario) into the ready-to-drink product, in the form of powdered tea (second scenario) and boxed tea (third scenario). This study simulated an integrated system of tea product manufacturing system with biogas utilization produced from tea organic waste. Simulation conducted based on daily manufacturing process at the Gamboeng green tea factory. Additional required energies were simulated from the wood pellet, which is the best practice in the Gamboeng Tea factory. By shifting tea consuming habit from dried tea to powdered tea and/or boxed tea, the emission from a cup of tea can be reduced, with range of reduction varied from 8.87 g to 22.13 g CO2-eq per cup of tea. If the Gamboeng green tea daily production capacity of the factory is fully converted into powdered tea, the potency of CO2 emission reduction reaches 26.92 metric ton CO2. However, the factory should pay attention to providing the water for the manufacturing process. The required water was 45.23 m3 of drinking water if all dried tea converted to powdered tea. Moreover, 11.53 m3 of water is required as irrigation for the biogas process in converting all tea organic waste into biogas. [ABSTRACT FROM AUTHOR]

The objective of this study is to use a life-cycle assessment (LCA) to compare and evaluate the environmental impact of combinations of open lagoon technology (COLT) with composting and COLT-Biogas for palm oil mill effluent (POME) treatment in North Sumatera, Indonesia. COLT-Biogas technology consists of three major types: composting, land application, and membrane technology. The environmental impact was evaluated by focusing on global warming potential (GWP), acidification potential, eutrophication potential, human toxicity potential, energy consumption (EC), and net energy ratio (NER). The results presented herein indicate that the GWP was the most significant criteria in the POME environmental impact assessment. With respect to GWP, COLT-Biogas A integrated with composting was observed more environmentally friendly than the other combinations. Approximately 357.18 kg CO 2-eq of GWP reduction could be achieved with this technology without considering the utilization potential of its products. With respect to NER, COLT-Biogas B with land application and COLT-Biogas C with membrane technology achieved better results since they do not require fossil fuel input. In terms of EC, COLT Biogas C with membrane technology required the lowest amount of energy among the alternative technologies. A novel approach of comparing POME treatment technologies is presented herein, and the findings could help decision makers and planners to adopt the more environmentally friendly policies, resulting in more sustainable palm oil products. [ABSTRACT FROM AUTHOR]

• Raman spectroscopy was used to analyze carotenoid concentrations in tomatoes. • Partial least squares regression models were developed for quantitative analysis. • Partial least squares discriminant analysis (PLS-DA) models were developed. • Raman exposure time affects the ability to quantitatively analyze tomatoes. • Discriminate analysis is largely unaffected by Raman exposure time. The significant worldwide expansion of the health food market, which includes functional fruits and vegetables, requires a simple and rapid analytical method for the on-site analysis of functional components, such as carotenoids, in fruits and vegetables, and Raman spectroscopy is a powerful candidate. Herein, we clarified the effects of Raman exposure time on quantitative and discriminant analysis accuracies. Raman spectra of intact tomatoes with various carotenoid concentrations were acquired and used to develop partial least squares regression (PLSR) and partial least squares discriminant analysis (PLS-DA) models. The accuracy of the PLSR model was superior (R2 = 0.87) when Raman spectra were acquired 10 s, but decreased with decreasing exposure time (R2 = 0.69; 0.7 s). The accuracy of the PLS-DA model was unaffected by exposure time (hit rate: 90%). We conclude that Raman spectroscopy combined with PLS-DA is useful for the on-site analysis of carotenoids in fruits and vegetables. [ABSTRACT FROM AUTHOR]

A comparative evaluation of economic efficiency was performed for native polyculture microalgae oil production in an oxidation ditch (OD) process wastewater treatment plant (WWTP). A cost function was developed for the process. The operational cost per 1 m3 of wastewater (w.w.) was 1.34 $/m3-w.w. in the existing scenario, 1.29 $/m3-w.w. in algal scenario A (no cost for CO2 and waste heat) and 1.36 $/m3-w.w. in algal scenario B (no cost for CO2). The conditions were set as follows: hydraulic retention time (HRT): 4 days, microalgal productivity: 0.148 g/L and daily treatment volume: 81.6 m3-w.w./d. The cost differences were related to the increase in polymer flocculants for algae separation (+0.23 $/m3-w.w), carbon credits from CO2 absorption (−0.01 $/m3-w.w), the sales of biocrude (−0.04 $/m3-w.w) and sludge disposal (−0.18 $/m3-w.w). Hence, the introduction of the algae scenario was the same cost-effective as the existing scenario. Microalgae oil production in an OD process WWTP can serve as a new energy system and reduce the environmental load in a society with a declining population. [ABSTRACT FROM AUTHOR]

Sugarcane harvesting requires a significant amount of energy and time to manage dry leaves after the harvesting process. Therefore, the objective of this study was to minimize the energy requirement to process the cane and dry leaves' harvesting (CDLH) for sugarcane while, at the same time, maximizing sugar production from cane and energy from dry leaves in Sri Lanka. The CDLH was conceptualized using a novel approach to optimize sugarcane harvesting to maximize biomass supply for energy production while reducing supply chain sugar-loss. The CDLH was investigated for manual harvesting capacity, energy consumption, sugar loss, and biomass energy potential. It was observed that CDLH consumed higher energy compared to the present practices of harvesting. However, the energy used for fieldwork was reduced because of the shifting of cane chopping and cleaning from the field to the factory. Low bulk density of the harvested cane of the CDLH system had a higher energy requirement in transportation. Comparatively, CDLH showed higher biomass energy potential and less sugar loss. High energy potential increases the energy potential to consumption ratio compared to the existing method. Therefore, the theoretical evaluation showed that the CDLH system can produce more than 20 kg of sugar and 879 MJ of electricity when processing 1 t of sugarcane. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications. [ABSTRACT FROM AUTHOR]