Nakawajana, Natrapee

Preferred name

Alternative Name

Nakawajana, N.

Main Affiliation

KMITL Prince of Chumphon Campus

natrapee.na@kmitl.ac.th

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
The energy potential evaluation of biomass fuel from weed pellets
(2023-03-01)
Saechua, W.
;
Posom, J.
;
Hongwiangjan, J.
;
Nakawajana, N.
The giant sensitive plant had the most appropriated qualifications for pellet production, as it contained a low content of moisture (3.07%) and ash (2.68%), and a high percentage of fixed carbon (14.13%) and volatile matter (80.12%) with the highest lower heating value of 18,334.08 J/g. It was found that a higher mixing ratio of water for pelletization led to increasing moisture and ash content but resulted in lower volatile matter and fixed carbon. The best mixing ratio for was 8% of water by weight, at which the evaluated properties of the giant sensitive plant pellets: LHV, FC, VM and ash content, were 18,747.46 J/g, 10.92%, 78.77% and 2.24%, respectively. The evaluation of the physical properties of the pellets revealed that a higher amount of water resulted in a larger diameter, greater length and higher fines content. Comparison of pellets from this work with the standard classes of biomass pellets found that the giant sensitive plant pellets could be classified in class I3. It was clearly seen that the weed can be utilized as a useful biomass fuel and formed in a pellet shape for combustion in industrial sectors.
Some of the metrics are blocked by your
consent settings
Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp
(2021-11-01)
Saechua, Wanphut
;
Sharma, Sneha
;
Nakawajana, Natrapee
;
Leepaitoon, Kritsanaphon
;
Chunsri, Rashphon
The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (R<inf>C</inf>2), and prediction (R<inf>P</inf>2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.
Some of the metrics are blocked by your
consent settings
Characterization and Application of Biochar Derived from Snake Fruit Peel for Lead Adsorption
(2024-01-01)
Maneesri, Wisit
;
Choolaaied, Orasa
;
Phanchindawan, Naree
;
Ketpimol, Nopadol
;
Limmun, Warunee
Lead (Pb(II)) is a prominent contaminant in industrial wastewater, causing environmental and health risks. Traditional treatment methods often encounter limitations, including high operational costs and low efficiency in dilute solutions. This study presents an innovative, cost-effective solution utilizing biochar derived from snake fruit peels. Two biochar materials, SB500 and SB700, were produced via pyrolysis at 500 °C and 700 °C, respectively. The results indicate that the physicochemical properties of biochar change with increasing pyrolysis temperature. In addition, adsorption kinetics experiments showed that the two biochars displayed rapid adsorption within the first 60 min, with adsorption capacities of 28.08 mg/g for SB500 and 26.68 mg/g for SB700. This behavior can be attributed to a combination of physisorption and chemisorption mechanisms. These findings highlight the significance of the surface properties of biochar, especially its mesoporous structures and functional groups. Furthermore, this study suggested developing an efficient approach to mitigating the environmental and health impacts of Pb(II) contamination while addressing the issue of agricultural waste management.
Some of the metrics are blocked by your
consent settings
Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression
(2022-12-01)
Pornchaloempong, Pimpen
;
Sharma, Sneha
;
Phanomsophon, Thitima
;
Srisawat, Kraisuwit
;
Inta, Wasan
Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.
Some of the metrics are blocked by your
consent settings
A low-cost system for moisture content detection of bagasse upon a conveyor belt with multispectral image and various machine learning methods
(2021-05-01)
Nakawajana, Natrapee
;
Lerdwattanakitti, Patchara
;
Saechua, Wanphut
;
Posom, Jetsada
;
Saengprachatanarug, Khwantri
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2 ) 0.72, root mean square error of prediction (RMSEP) 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.

Nakawajana, Natrapee

Filters

Settings

Sort By

Results per page