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Energy Procedia 142 Energy Procedia 00(2017) (2017)2977–2982 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Forecasting of Refined Palm Oil Quality using Principal Component The 15th International Symposium on District Heating and Cooling Regression a Assessing feasibility of Rosely usinga, the heat demand-outdoor Nor Adhihah Rashidthe , Nur Atikah Mohd Mohd. Aiman Mohd. Noora, Azmer a a,b a,b temperature for a long-term district heatAsri demand Shamsuddinfunction , Mohd. Kamaruddin Abd. Hamid , Kamarul Ibrahimforecast * aa Process
Process System Engineering Centre (PROSPECT), Rresearch Institute of Sustainable Environment (RISE)
a,b,c a a b c I. Andrić Pina , P. Ferrão , J. Fournier ., 81310 B. Lacarrière , O. LeMalaysia Correc of Chemical*, andA. Energy Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Faculty
bb Faculty a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract
Abstract
Over the past few decades, Malaysia has led the world in terms of production and export of palm oil. Driven by the prolific growth of palm oil industry, the quality of refined palm oil has become one of the predominant parts in related palm oil based Abstract industries. However, if the quality does not meet the standard, the out-specification refined palm oil needs to recycle back to the deodorization tank. The equipment cost, worker salary, processing time, and energy are all the expenses needed to recycle the District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the out-spec palmgas oil emissions which potentially a great loss These in timesystems and costrequire for thehigh plant.investments Therefore, which the goal this study is to develop greenhouse from thebeing building sector. areofreturned through the heat a sales. principal component regression (PCR)-based model to predict the quality of refined palm oil. The variables; Free Fatty Acid Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, content (FFA), Iodine Value (IV) and Moisture Content (MOIST) are used to build the prediction model. Comparison of PCR prolonging the investment return period. predicted industrial data was made. It was proven in this that PCR– can be used to estimate the quality of refined The mainresult scopewith of this paper is to assess the feasibility of using thestudy heat demand outdoor temperature function for heat demand palm oil. . By having this predictor, the quality of the refined palm oil can be guaranteed thus all expenses related to recycling forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted ofout 665 of specification refined palm oil such as energy, salary, can be saved. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district ©renovation 2017 The Authors. by Elsevier Ltd. intermediate, deep). To estimate the error, obtained heat demand values were scenariosPublished were developed (shallow, © 2017 The under Authors. Published by Elsevier Ltd. committee of the 9th International Conference on Applied Energy. Peer-review responsibility of the scientific compared with results from a dynamic heat demand model,of previously developed and validatedon byApplied the authors. Peer-review under responsibility of the scientific committee the 9th International Conference Energy. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications
Keywords: Quality Forecasting; Refined Palm Oil; Principal Component Regression (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation
scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Kamarul „Asri Ibrahim Cooling. E-mail address:
[email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.364
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1. Introduction Palm Oil production is a complex process with many variables affecting the product quality. Natural variations in raw materials, interaction between different chemicals, the decisions of individual process operators and change to process settings between palm oil standard qualities, all complicate the goal of achieving consistent, optimized product. The out specification standard quality of RBDPO (Table 1) will be recycled until the desired quality is achieved. The recycle process not only resulted in loss of profit due to no production, on top of that, the company has to spend nearly RM 10,000 per month to ensure the product quality meets the customer standard [1]. Table 1. Standard quality of CPO and RBDPO Quality Variables
CPO
RBDPO CHINA
PORAM
VIETNAM
Free Fatty Acid, FFA (%)
1 should be retained as it represent the large amount of variance, explained by the original variables. After getting the PC‟s, the initial data set is transformed into the orthogonal set by multiplying the eigenvectors to the initial data set. This transformed data set was used as input to the multiple linear regression technique.
Y k 0 k1 ( PC1 ) k 2 ( PC 2 ) k n ( PC n ) e
(3)
Where k is the relationship coefficients model estimated using the least square method. Principal components were computed using the training data and also used as an input to regression model to form PCR model. The PC‟s were determined on the basis of the variance explained by the eigenvalue of the data matrix. Only those PC‟s whose eigenvalues based on the analysis of 3 input variables, were used to predict the quality of RBDPO. Consequently, the network is less complex in PCR due to the decrease number of input variables into multiple linear regressions. Once this stage has been completed, the performance of the PCR model was validated with independent variables of testing data. The accuracy of the model was analyzed through mean square error calculation. 3. Results and discussion The best sample size was found to be 35 sample size which providing the most similar distributed trend and least outliers (Figure 1a). From the boxplot, there were a few potential outliers from the FFA input and output, MOIST input and output and IV output. All the quality variables were autocorrelated to identify the optimum sampling time by inspecting the longest lag that fell below the threshold level among the autocorrelation graphs. Based on Figure
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1(b), it was the first lag that fell below the threshold level. So, by multiplying the lag with the time interval of the data collected, the optimum sampling time observed was 4 h. The staggered cross-correlation technique allowed us to identify the optimum processing time. By inspecting the staggered cross-correlation graph (Figure 1c), the longest lag that intercept with the zero y-axis, 3 lags were multiplied to 4 h of optimum sampling time resulted in 12 h optimum processing time. The data was staggered according to the optimum processing time lag. a
b
c
Fig. 1. (a) Boxplot of 35 sample size; (b) Autocorrelation of 35 sample size; (c) Cross-correlation of 35 sample size.
Once the standardized, staggered data was obtained, the predictor tool was developed according to the model. Hereby, the PCA was run on the CPO quality variables to extract the PC scores and loadings which then became the input of multiple linear regressions. The relationship coefficient (k) was determined from the regression of the RBDPO quality variables and the PC‟s. After all the predicted output had been calculated, the Mean Squared Error (MSE) was calculated between actual and predicted value of output to observe the effectiveness and efficiency of the prediction model. The closer the value of MSE to zero, the better the prediction is. As shown in Figure 2, the MSE value of training data for all quality variables were closed to zero, depicting that the prediction was fairly good.
Nor Adhihah Rashid et al. / Energy Procedia 142 (2017) 2977–2982 Nor Adhihah Rshid/ Energy Procedia 00 (2017) 000–000 a
MSE of FFA
6.20E-05 6.00E-05
5.80E-05
b
c 0.003
8.00E-06
0.0029
5.60E-05
6.00E-06
5.40E-05
4.00E-06
5.20E-05
MSE of MOIST
1.00E-05
5.00E-05
2.00E-06
4.80E-05
0.00E+00
2981 5 MSE of IV
0.0028 0.0027 0.0026
0.0025 0.0024 MOIST
4.60E-05 Training data
Testing data
Training data
Testing data
IV Training data
Testing data
Fig. 2. Mean Squared Error for training data and testing data of (a) FFA; (b) MOIST; (c) IV.
To further validate the developed tools, the model was run using a total of 90 industrial data (testing data) from Lahad Datu Edible Oils Sdn. Bhd. The MSE calculated for testing data were expected to have greater values than that of the MSE calculated from training data (Figure 2), since the prediction model was developed based on the training data. Although the MSE for testing data was slightly higher than the training data, the value was still close to zero, thus, it was proven that the PCR method was effective in predicting the quality of RBDPO. Control charts were plotted to determine which standard quality the RBDPO belong to and also to observe the process behavior. The control charts were plotted for the actual output and predicted output of testing data as shown in Figure 3. Despite of having some out-control process; the prediction was acceptable as long as the value did not exceed the standard limit. As shown in the figure 3(a), there was no point fall outside the process control limit (red solid line), but the predicted quality was belong to the CHINA standard quality (
