Application of Near-Infrared for Online Monitoring of Heavy Fuel Oil at

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General Research

Application of near infrared for on-line monitoring of heavy fuel oil at thermoelectric power plants. Part I: Development of chemometric models Ayslan Santos Pereira da Costa, Josefa Manuela Santos Gonçalves, Hosana Oliveira Ávila Neta, Douglas R. M. Alves, Everton S. Lourenço, Elton Franceschi, Claudio Dariva, Vanias Araujo, Alexandre Venceslau, Manuela Souza Leite, and Gustavo Rodrigues Borges Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b02107 • Publication Date (Web): 01 Aug 2019 Downloaded from pubs.acs.org on August 5, 2019

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Industrial & Engineering Chemistry Research

Application of near infrared for on-line monitoring of heavy fuel oil at thermoelectric power plants. Part I: Development of chemometric models Ayslan S.P. Costa1,2, Josefa M.S. Goncalves1,2, Hosana O. Ávila Neta1,2, Douglas R. M. Alves1, Everton S. Lourenço1,2, Elton Franceschi1,2, Claudio Dariva1,2, Vanias Araujo3, Alexandre Venceslau3, Manuela S. Leite1,2, Gustavo R. Borges1,2* 1

Center for Studies on Colloidal Systems (NUESC)/Institute of Technology and

Research (ITP), Av. MuriloDantas, 300, Aracaju-SE, Brazil, CEP 49032-490. ²Tiradentes University (UNIT), Postgraduate Programme in Process Engineering (PEP)/ Postgraduate Programme in Industrial Biotechnology (PBI), Av. Murilo Dantas, 300, Aracaju-SE, Brazil, CEP 49032-490. 3

Energética Suape II S.A, Rodovia PE 060, km 10, n° 8.100 – Complexo Portuário de

Suape, Cabo de Santo Agostinho–PE, Brazil, CEP 54510-350.

KEYWORDS: NIR, heavy fuel oil, ANN, chemometric models, thermoelectric power plants

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ABSTRACT:

In Brazil, thermoelectric power plants (TPP) are responsible for about 25% of the electric energy generated. Of the energy supplied by the thermoelectric plants,11% comes from the burning of heavy fuel oils (HFO). The monitoring of these oils is of great industrial interest in order to maintain their quality, avoid damage to the engines, and guarantee the energy supply. In this sense, this work investigated the potential of near-infrared (NIR) spectroscopy combined to partial least-squares (PLS) and artificial neural network (ANN) models for on-line monitoring of temperature, density, water content and kinematic viscosity of the HFO used at Energética Suape II S.A.. Chemometric models were calibrate to predict the properties of interest in the temperature range typically found at thermoelectric power plants (from 25 to 120 °C).The results show that NIR spectroscopy combined with multivariate calibration is a powerful tool for on-line monitoring of HFO. ANN models presented a better performance than PSL models, mainly for non-linear properties like kinematic viscosity, however, all determination coefficients were bigger than 0.95 for all properties when compared to standard methods. The next step of this work will comprise the installation of an industrial NIR at TPP and evaluate the models performance when applied in real industrial process.

1.

Introduction Due to the world socio-economic growth, the International Energy Agency

estimates that 56% of the world's electricity in 2017 came from non-renewable resources. This percentage is equivalent to about one quarter of the total energy supply in the OECD (Organization for Economic Cooperation and Development) countries1–3. 2 ACS Paragon Plus Environment

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In Brazil, where electricity is generated predominantly by hydroelectric plants, thermoelectric power plants (TPP) are the second most important form of generation in the country, responsible for 25% of the Brazilian electric energy. Currently in Brazil, 77 TPPs are powered by heavy fuel oil (HFO), generating approximately 3352 MW, being TPP Mauá, located in the state of Amazonas, the largest power generating plant with an installed capacity of 462 MW and TPP Energética Suape II S.A., located in the state of Pernambuco, the largest power plant in terms of generation with 381.2 MW (BIG, ANEEL 2019). The heavy fuel oil (HFO) used by the Energética Suape II S.A. thermoelectric power plant consists of a crude oil fraction derived from vacuum distillation, containing mixtures of hydrocarbons and heteroatoms compounds containing (S, N and O) and metals. This by-product or residue has an composition dependent on the original oil and the subsequent distillation processes, so, the refineries can produce HFO with different physicochemical properties3–5. Besides being used in combustion engines for electricity production, HFO is also used in industrial gas turbines, fired furnaces and boilers, in maritime transport for large vessels, and in industrial blast furnaces. Thus, there is a growing interest in the comprehensive study of HFO, since this fuel accounts for a significant share of the energy consumption in various parts of the world6–8 The importance of on-line monitoring the properties of HFO arises from environmental and operational concerns regarding engines. The previous knowledge the physicochemical properties such as density, viscosity and water content (WC) can be useful to optimize the combustion conditions reducing particulates and greenhouse gases emissions, as well as avoiding engines damages4,6–9.



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Analytical methods based in near-infrared (NIR) spectroscopy and multivariate calibration models have important characteristics for industrial applications because they are rapid, non-destructive and non-invasive methods that can be used to obtain various direct and comprehensive information in a single analysis. The NIR probes can be installed easily with optic fibres, which facilitate their practical in-line implementation in harsh process conditions, due to the resistance of the probes to high temperature and pressure10,11. Principal components analysis (PCA) and partial least squared (PLS) have been widely used as multivariate calibration methods for regression of NIR spectrum to a quantitative system information when a linear pattern is stablished12–14. However, in situations of non-linear behaviour, such as non-adherence to the Beer-Lambert law at high concentrations of analyte, a non-linear response of the detector and the dispersion of the light source may occur15. In this cases, algorithms based on artificial neural networks (ANNs) are more indicate to modelling such systems.16–18 In recent years, researchers have shown the potential of infrared spectroscopy techniques together with multivariate calibration models, such as PLS for prediction of physicochemical properties of crude oil and their derivatives10,19–22. Notable are the studies reported by Laxalde et al.23, who combined mid-infrared (MIR) and NIR spectroscopic technique for determination of Saturate, Aromatic, Resin and Asphaltene (SARA) content in heavy oils using adaptations of the PLS model. Dupuy et al.24, used NIR spectroscopy and chemometric techniques to predict some physicochemical properties of heavy fuel oil, such as viscosity, density, water content, nitrogen, vanadium, sulfur and carbon residue contents using reflectance techniques on samples of fuel oils on fiberglass cell support at fixed temperature. However, even with the considerable amount of study into the application of NIR in petroleum and derivatives, 4 ACS Paragon Plus Environment

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there is still a lack of studies that present details of the application of this technique in heavy fuel oil, especially in the context of an on-line industrial application under different temperature conditions. This study aims, firstly, to evaluate the potential of near infrared spectroscopy combined with chemometric models to on-line monitoring of HFO physicochemical properties (temperature, water content, density and kinematic viscosity) at different temperatures typically found at TPPs. It should be emphasized, that this is the first work that proposes the use of NIR combined with chemometric models to monitor HFO physicochemical properties in a wide temperature range (25-120 °C). This fact is industrially and academically relevant, once the developed models can be applied at different conditions found in real process that employs HFO. In a second step, the developed models will be tested at real situations in TPP Energética Suape II S.A. NIR probes will be installed in different points of interest enabling an on-line HFO monitoring. The results of this step will be presented in the next paper.

2.

Materials and Methods

2.1 Heavy Fuel Oil HFO samples used in this study were supplied by Energética Suape II S.A. which were classified according to the ANP (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis) as OCB1 due to its characteristics (viscosity around 620 cSt at 60 ºC and maximum sulfur and water content of 1 and 2%, respectively). Figure 1 shows a flowchart of the HFO path in Energética Suape II S.A. Thirty-two samples were collected during one year from different HFO´s lots at two points where there is the greatest need to monitor the properties of the oils. The first collection point is 5 ACS Paragon Plus Environment


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located at reception area (Area I) aiming to obtain samples as provided by supplier without any treatment and the second collection point selected was after HFO treatment (Area II) aiming to evaluate the efficiency of treatment process which consist of an HFO centrifugation to remove water and solid materials before burning at engines. From those 32 collected samples, 15 were selected based on its physicochemical characteristics (Figure S1) and employed to develop the chemometric models. This strategy was adopted aiming to avoid the use of similar samples to construct the models and also to optimize the experimental work, once each sample is submitted to different temperatures (typically 20 points at the interval from 25 to 120 °C) generating a great number of experimental conditions and, consequently, NIR spectra.

Figure 1. Flowchart of the Energética Suape II S.A. process from the arrival of HFO to the generation of electricity energy in the engines.

In the process shown in Figure 1, the two highlighted points correspond to the local where the monitoring of the HFO samples will be performed by NIR. The first point, identified as a low-temperature region (average ambient temperature 30 °C), is the step of HFO monitoring upon arrival at the plant. This oil is stored in preheated tanks (~60 °C) and then directed to a centrifuge treatment to remove water, sludge and 6 ACS Paragon Plus Environment

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sediments. The second HFO monitoring point will be installed at the outlet of the centrifugal treatment, where the average oil temperature is 95 °C. With installation of the monitoring probes, it will be possible to evaluate the efficiency of the treatment process by on-line monitoring of HFO properties such as density, viscosity and water content ensuring that the oil meets the operating standards before being burned in the internal combustion engines to generate electricity.

2.2 Characterisation of HFO samples The physicochemical properties of the 15 distinct HFO samples were measured in the laboratory aiming the correlation between the experimental data and the NIR spectra. In order to verify the water content (WC) of the HFO samples, a coulometric titration of Karl Fischer reagent was used (DL 39, Mettler Toledo). This equipment allows analysis in a range of 0 to 5% with precision better than 0.03%. The experimental values of density were obtained using a densimeter (DMA 4500 M, Anton Paar) with a measurement capacity of 0 to 3 g.cm-³, providing an accuracy of 0.00005 g/cm³. This equipment allows testing with temperature ranging from 0 to 100 ºC. The methodology is the standard test method ASTM D5002-16. For the viscosity measurements, a rheometer (PhysicaRheolab MCR 301, Anton Paar) was used at temperatures from 25 to 120 °C at intervals of 5 °C.

2.3

Near-Infrared spectroscopy NIR spectra were acquired using a spectrophotometer (Bruker, MPA FT-NIR)

interconnected by a pair of optical fibre to a transflectance probe with optical pathlength 7 ACS Paragon Plus Environment


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adjusted to 1 mm for samples with water content above 1%, and 2 mm for oils with percentages below 1%. The spectra were collected over the entire NIR wavelength (12000–4000 cm–1) with a resolution of 8 cm–1, with each spectrum consisting of the mean of 16 scans. The spectra of the HFO samples were collected in the temperature range between 25 and 120 °C. At least six spectra were collected at each temperature investigated, typically at intervals of 5 °C or less, employing air as reference. Further information on NIR spectrophotometer stability/reproducibility can be found at Figure S2.

2.4. Experimental apparatus In order to simulate the HFO flow into the pipelines in the plant processes, an apparatus for obtaining spectra was developed where the NIR probe was connected to a flow-cell immersed in a thermostatic bath with temperature control, as shown in Figure 2.

4 5 1 2

3

6

7

Figure 2. Schematic diagram of the experimental apparatus for HFO monitoring in continuous flow by near infrared spectroscopy. 1 – NIR spectrophotometer and personal computer, 2 – sample collection, 3 – NIR flow cell, 4 – NIR probe, 5 – Thermocouple with universal indicator, 6 – Thermostatic bath and 7 – Manual HFO pump.


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The apparatus consisted of a positive displacement pump (7) with a capacity of 400 mL where the HFO was pumped into a set of pipelines immersed in a thermal bath (6) at a flow of approximately 10 mL.min–1. A J type thermocouple connected to an universal indicator (5), whose precision is better than 0.1 °C, was placed before the flow cell (3) where the NIR probe (4) was connected. At the outlet, the oil was discarded into a beaker (2). A personal computer connected to the NIR spectrophotometer (1) was applied to spectrum collect and development of chemometrics models.

2.5

Chemometric models All spectra were submitted to a pre-treatment process of standardisation or

derivative processes to favour a good fit of the PLS models. The standard normal variate (SNV) preserves only the structure of the spectrum, while the first derivative is used to highlight small characteristics in the spectra that are not possible to visualise due to the large absorption bands. All pre-treatment procedures and adjustments of the chemometric models were performed by OPUS® software using the QUANT2 tool supplied by Bruker. After the pre-treatment, the available spectra were divided into two distinct sets: approximately 70-85% of the samples were used for calibration of the PLS model and the remaining 15-30% for model tests. In this algorithm, the X and Y data matrices are correlated to each other, by means of linear adjustments, where the matrix X corresponds to the absorbance of the sample at each wavelength and Y is the property of interest4. In this study, the PLS algorithm was used to perform calibration and regression to determine the temperature, density, viscosity, and WC in flowable HFO at different temperatures. 9 ACS Paragon Plus Environment


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For the development of the model based on ANNs, each spectrum obtained by the NIR was represented by a vector of 1 × n, where n represents the wavenumber (with a resolution of 8 cm-1) referring to the region selected in the PLS models adjusted for each property of interest, as presented at Table 2. The spectrum set was subjected to pre-treatment of SNV, 1st derivative, 2nd derivative, or combination of the methods depending on the property to be modelled. Then, the data dimensions were reduced using the PCA, obtaining a smaller set (minimum of 2 components) that represent at least 90% of explained variation of the original data set. From this new set of variables, the PCA allows the finding of similarities among samples and to detect samples that are not within the standard behaviour of the others (outliers). The PCA method has great advantages and has been widely applied to fields that involve many variables11,25–27. After defining the minimum set of principal components that explained the maximum variations in the X matrix of spectral data, these principal components, were used as input variables for the construction of the neural network. Matlab® software version 2015 was used to obtain the main components and to develop the neural model. In this study, models based on ANNs of the back-propagation type with Levenberg–Marquardt and Bayesian regularisation training algorithms were used. ANN architecture has been successfully applied in studies involving the prediction of physical-chemical properties of petroleum and derivatives.6,16,17,28,29 The definition of the number of neurons in the hidden layer was determined as a function of the smallest error generated in the variation between 4 and 10 neurons, using the linear or hyperbolic tangent transfer functions16,30–32.


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2.6

Models evaluation. In order to evaluate the performance of the models proposed in this study the

root mean square error (RMSE), presented in Equation 1, was used in two instances. First, the RMSE was evaluated in the adjustment phase of the models in the crossvalidation process (RMSECV). Later, the same equation was applied at the test stage of the model, providing the prediction error (RMSEP). In the model testing step, the predicted values are a new set of data (between 15% and 30% of the total set) not used in the construction of models (training step)33–36.

𝑀

1 𝑒𝑥𝑝 𝑝𝑟𝑒𝑑 𝑅𝑀𝑆𝐸 = √ . ∑(𝑌𝑖 − 𝑌𝑖 )² 𝑀

Eq. 1

1

where, 𝑌𝑖𝑒𝑥𝑝 is the experimental value obtained by the reference method,𝑌𝑖𝑝𝑟𝑒𝑑 is the results predicted by model and M the amount of sample (spectra) analysed. It is desirable that models provide low values of RMSE. In addition, the coefficient of determination (R²) was used to measure the model´s performance (PLS and ANN) for both training and test steps and chi-square statistical analysis was employed to compare the models.

3.

3.1.

Results and Discussion

Characterisation of heavy fuel oil. Table 1 reports the properties ranges for HFO samples used in this study. The

viscosity and density variation (for the same sample) was in function of temperature variation, while water content is constant for each sample. 11 ACS Paragon Plus Environment


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Table 1. Experimental values (minimum and maximum) for each HFO sample investigated in this work.

Sample HFO-1 HFO-2 HFO-3 HFO-4 HFO-5 HFO-6 HFO-7 HFO-8 HFO-9 HFO-10 HFO-11 HFO-12 HFO-13 HFO-14 HFO-15

Temperature (ºC) Min Max 40 90 25 114 40 90 26 115 30 115 40 95 40 95 25 105 25 120 25 95 20 115 20 120 27 121 28 105 25 115

Range Viscosity (cSt) Density (g.cm-³) Min Max Min Max 53.5 590.0 0.9152 0.9518 17.1 4797.0 0.8846 0.9469 47.0 799.6 0.9182 0.9513 22.6 2558.7 0.9055 0.9678 18.3 2075.0 0.8948 0.9494 40.8 713.4 0.901 0.9365 39.6 590.5 0.9065 0.9425 31.1 3965.8 0.9204 0.9734 15.3 4327.5 0.8943 0.9513 8.5 454.8 0.8787 0.9207 4.2 190.4 0.8425 0.9185 20.6 4016.4 0.8876 0.9558 24.1 5058.4 0.8830 0.9491 34.0 3865.5 0.89391 0.9448 28.5 3436.4 0.9027 0.9657

WC (%) 0.08 0.11 0.98 0.27 0.08 0.16 0.17 0.25 0.11 0.14 0.06 0.57 0.2 0.37 0.21

All tests were made in triplicate to provide better evaluation of the errors of the analyses. It should be emphasized that HFO viscosity presented a non-linear behaviour regarding to temperature (please, see Figure 8). At low temperatures, as expected, the HFO viscosity presents high values and decreases quickly when the temperature increases up to 60 °C. For highest temperatures (up to 120 °C) the viscosity decreases gradually with temperature increase. On the other hand, HFO density presented a linear decrease with temperature increase in the entire range investigated, indicating that there are no phase transitions in the system. These experimental values were then used to fit the chemometric models PLS and ANN. It should be emphasized, that the range evaluated for each variable was selected aiming to cover all situations found at industrial process lines.


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However, it is possible to notice that the WC in the HFO samples are low and does not suffer much variation, indicating a good result for the industry. On the other hand, for the development of the chemometric models for this variable, it is necessary samples with different WCs and comprising the entire range of industrial interest. As the maximum percentage of water allowed for heavy fuel oil OCB1 is 2%, emulsions were synthesised in the laboratory to construct a complete and representative dataset for the development of the models for WC. The emulsions were synthesised according to the methodology presented by Fourtuny et al.37, with water content of 0.3, 0.4, 0.6, 0.8, 1.0, 1.2, 1.6, 1.75 and 2 wt%. These new samples were only applied to fit the chemometric models for water content together with those presented at Table 1, totalizing 24 samples.

3.2.

NIR spectra Figure 3 presents the NIR spectra of HFO-13 at different temperatures from 30

to 120 °C. As can be seen, the spectra base line decreases when temperature increase indicating a lower absorbance of NIR irradiation by the sample at high temperature. This fact can be related to the samples density which also decreases with temperature increase. All the samples investigated in this work presented similar NIR spectra. Perceptible changes with variation of the oil type was found at the region from 6500 cm1

to 9000 cm–1. This region of the spectrum presents higher levels of noise and elevation

of the baseline, which may be related to the presence of particulates or electronic absorption corresponding to transitions of asphaltenes molecules23. As a result, oils with higher percentages of asphaltenes and particulates tend to show an increased baseline slope and spectrum noise at this region. The noise at regions above 9000 cm-1 can be attributed to the limitation of NIR spectrophotometer when a dark sample is analysed. 13 ACS Paragon Plus Environment


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Figure 3. Near infrared spectra of HFO-13 at temperatures ranging from 30 to 120 ºC.

The absorption bands between 4000 and 4500 cm–1 correspond to the combination of stretching vibrations of the C–H group or bending of CH2 and CH3. Then, it perceives a low absorption (close to 4800 cm-1) that can be assigned to the combination of fundamental vibration in unsaturated groups. The region between 5500 and 6000 cm–1 represents the first overtone of the fundamental C–H stretching, corresponding to methyl, methylene and ethylene groups and around 7000 cm–1, where the absorption is weaker, there are the second overtone of fundamental vibrations of C–H bonds23,24. To use the spectrum to adjust the chemometric models via PLS or ANN, it was necessary to select a region or regions that best correlates with the physicochemical property of interest. The use of the whole spectrum can cause instability in the model, due to the noise variations presented in regions above 9000 cm‑1. Thus, models obtained with restricted wavelength simplify the interpretation allowing more accurate and robust predictions38,39. The selection of these regions was made by means of “Optimize” tool available in the OPUS® software and may vary depending on the property of interest. The Optimize tool suggests the best combination of spectral region 14 ACS Paragon Plus Environment

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and pre-processing to obtain the lowest RMSECV. The models were firstly constructed by PLS algorithm, which is the most common chemometric method for NIR spectra adjustments, and then ANN models were applied as an alternative for nonlinear property adjustments where PLS fails4,10.

3.3

Temperature model Temperature is an important parameter for thermoelectric plants that operate with

HFO because the oil should be maintained at prespecified temperature (typically bigger than 60°C) to guarantee low viscosity (< 300 cSt in case of Suape) and attend the pumping system and burning specification. Although be a parameter easily monitored at industrial process by thermocouples, for example, NIR spectrophotometer can be also calibrated to system temperature monitoring, once the same spectra data set is employed for build all the models of interest. One can note at Figure 3 that the NIR spectra changed significantly with increasing temperature, so the PLS regression model was used to correlate this variation. To adjust the model 1456 spectra from 15 different HFO samples with temperatures ranging from 25 to 120 ºC were used. This set of spectra was subjected to the SNV normalisation process followed by first derivative as preprocessing in the spectral region corresponding to wavelengths from 6101.5 to 4597.6 cm–1. To define the optimal number of latent variables and consequently the best adjustment of the model, 740 spectra were selected randomly for calibration and 716 for the model test. Figure 4A shows the relationship between the temperature predicted by the model and the experimental temperature in terms of cross-validation (o) and test (+). A good linear correlation between the temperature predicted by the PLS model and experimental values with R2 of 0.99, for the calibration and test steps, and 15 ACS Paragon Plus Environment


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RMSECV and RMSEP of 2.1 °C and of 1.7 °C, respectively, were obtained for the entire temperature range. These results indicate that the PLS regression model is able to predict the temperature of the oil at any point of application at thermoelectric plant, which is another advantage of the application of the NIR technique in the monitoring of industrial flows. Another example of NIR application for temperature monitoring was addressed in the studies presented by Shao et al.40, where the technique was used to predict temperature (in the range 25 to 60 °C) in mixtures containing different solvents (water, methanol, ethanol, n-hexane) and ethylenediamine with determination coefficients above 0.99.

3.4

Density model The importance of monitoring HFO density in a thermoelectric plant is directly

related to its kinematic viscosity, which is important for the design and control of the pumping system, as well as being a key parameter for calculations of the net calorific value of the oil41. For the development of this model the spectra collected with optical pathlength of 2 mm were used at temperatures between 25 and 120 ºC, totalling 1030 spectra. Aiming an improvement in the correlation of the spectra with this property, it was employed the SNV normalisation process followed by the first derivative in the spectral region between 5608.2 and 4455.9 cm–1 as pre-treatment of the spectra. The calibration of the density model was performed initially with 560 spectra. The density model showed good accuracy, reaching a R2 of 0.9894 between the density predicted by the model and the experimental density in the cross-validation and a RMSECV of


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0.00203 g.cm–3 with eight latent variables. Figure 4B presents the predicted versus experimental density values at cross-validation (o) and test steps (+), respectively.

A

B

Figure 4. Values obtained experimentally versus predicted by PLS model at temperature range from 25 to 120 ºC for: (A) HFO temperature (°C) and (B) HFO density (g.cm-3).

As only 560 spectra were employed for model development, the 470 remaining were applied to model validation. Analysing the figure one can confirm the generalisation capability of the model obtained over all temperature ranges with values of RMSEP of 0.0017 g.cm–3 and R² of 0.99 (Table 2). This result is satisfactory when compared to experimental error that was 0.0003 g.cm-3 and when compared with those obtained by Baird and Oja10 to predict density in shale oils used to feed power plants. The model proposed by the authors presented an RMSEP of 0.004 g.cm–3, analysed with temperature ranging from 15.6 to 90 ºC. In addition, the authors presented in their work that most models for predicting density for various fuels have a median error of 0.0018 g.cm–3.



3.5

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Water content model In this step, the data concerning the emulsions synthesized in our laboratory

together with those provided by Termoelétrica Suape II S.A. were used in order to provide models with ample prediction capacity within the limits required in operation and established by ANP for HFO samples classified as OCB1 (up to 2 wt%).Unlike the other models presented in this study (which were fit with spectra collected with an optical pathlength of 2 mm), the models for WC were developed using spectra obtained with probe of 1 mm of optical pathlength. This follows from the limitations in obtaining spectra of heavy fuel oils (or synthesised emulsions) with water percentages above 1% when using the 2 mm optical pathlength probe. Firstly, a general PLS model to estimate the water content in the entire temperature range (25 to 120 °C) was proposed (Figure 5A). For this PLS model a total of 1321 spectra were used being 903 for calibration and 418 for model tests. The second derivative as spectra pre-treatment was applied in the absorbance region between 5450 and 4597.6 cm-1. The model presented a R² of 0.945 for the cross-validation step and 0.964 for the model test with RMSECV of 0.12% and RMSEP of 0.11%, respectively. The biggest errors are observed mainly when the water concentration is above 1%. Aiming to increase the predictive capacity of the model and, consequently, reduce the prediction errors, two models were proposed to WC monitoring according to different temperature conditions at the process. The predicted vs experimental values presented at Figure 5B correspond to the performance of PLS model composed by spectra collected at temperature range from 25 to 45 ºC for all samples (totalizing 168 spectra), comprising the conditions typically found at HFO reception area (low temperature zone). The spectral range and pre-treatment applied in this model are 18 ACS Paragon Plus Environment

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presented at Table 2. This model presented similar correlation and medium error for cross-validation and test steps of 0.94 and 0.11%, respectively, for samples containing up to 2 wt% of water. Comparing these results with those of general model no significant improvements were observed. This may be related to the high viscosity of the HFO samples at this temperature range, which difficult the sample renovation at probe pathlength. However, both models can be employed to predict the water content in HFO with good precision for percentages up to 1%. This is an interesting result, once most of the samples collected at TPP Suape II SA presented water content below 1%, as can be seen at Table 1. A third PLS model for WC (Figure 5C) was developed to be applied after centrifugation process and before burning in the engine (high temperature zone). At this stage, the HFO temperature in the ducts is around 95 to 100 ºC. So, NIR spectra taken at a minimum temperature of 85 ºC and a maximum of 110 ºC from all samples were selected for model calibration, totalizing 396 spectra. The best correlations were obtained when the spectral region between 6101.9 and 4597.6 cm–1 was employed combining the pre-treatments of SNV and 1st derivative. According to literature, WC can be correlated with the vibrations of hydroxyl groups corresponding to wavelengths around 5200 cm–142. The prediction performance presented in Figure 5C shows that the model has good capacity to predict the experimental values of WC up to 2%, obtaining an R² of 0.975. The error in this prediction remained stable, with an RMSEP of 0.075, similar to the cross-validation and close to experimental error that was 0.02%. It should be emphasized that the spectra used to evaluate the predictive capacity of the model are fully independent of those used at calibration step.



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Figure 5. HFO water content (wt%) values obtained by Karl Fischer versus predicted by PLS models at different temperature ranges: (A) from 25 to 120 °C – General PLS model (B) from 25 to 45 °C and (C) from 85 to 110 °C.

Studies on models to determine WC in HFO are incipient in the literature. However, it is possible to compare the results presented in this study with those from models predicting the WC of petroleum, oil derivates or even effluents. For example, Filgueiras et al.33 studied the application of the ATR-FTIR spectroscopic technique combined with PLS multivariate calibration to predict the WC of petroleum. Even using wavelengths different from that used in our study (4000 and 646.10 cm-1), the model presented by the authors showed similar correlations within a range 0.1 to 6.1% water, reaching an RMSEP of 0.34. Zeng et. al.43 used the NMR technique, together with the PLS chemometric technique, for determination of water and oil in oil sludge. Besides the difference between the sources analysed and the equipment used, the authors studied 20 ACS Paragon Plus Environment

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the application of the technique to high percentages of WC (30 to 60%, approximately). However, the determination coefficients were similar to those presented in this study.

3.6

Kinematic viscosity model To construct the viscosity model, in a first approach, 1386 spectra comprising the

entire temperature range were employed. It was selected the region between 5450 and 4597 cm-1 and first derivative as pre-treatment. Of these spectra, 967 were selected to fit the model and the remainder were used later to test the model predictive efficiency. Figure 6A shows the plot of predicted and observed values for cross validation (o) and model test (+). It is evident that was not possible to obtain a suitable model for viscosity prediction over the entire temperature range. This model presented a determination coefficient and RMSE of 0.59 and 535 cSt for cross-validation and 0.725 and 462 cSt for test, respectively. The imprecision demonstrated by this model can be attributed to: the wide range of viscosity variation from 4.2 cSt (HFO-11 at 115 °C) to 5058.4cSt (HFO-13 at 27°C) and the inherent nonlinear correlation of this property with temperature variation, as can be seen at Figure 8. The same difficulty in fitting a PLS model to predict oil viscosity was presented by Rodrigues et al.44. The authors stated that the divergence between the viscosity values predicted by the model and the reference values can be attributed to the non-linear behavior of kinematic viscosity in function of temperature. Aiming to overcome this limitation, the same approach of spectra division by temperature range employed for WC models were applied for viscosity models. The second viscosity model was developed including spectra collected up to 45 °C from 15 different HFO samples (probe pathlength of 2 mm). Thus, 176 spectra were employed 21 ACS Paragon Plus Environment


Page 22 of 40

from which 146 were used in the development of the PLS model and 30 in the testing step. The best correlations were achieved when the region between 6101.9 and 4579.6 cm–1 was used applying SNV and the first derivative (in this order) as pretreatment. Figure 6B shows the comparison between predicted and experimental viscosity values for both validation and test step which obtained determination coefficients of 0.74 and 0.88 and RMSE of 247 and 131 cSt, respectively. A considerably increment on prediction capacity was observed for this model compared to general viscosity model, however, the RMSEP remains high when compared to the experimental error (29.3 cSt) and the model underestimate viscosities above 1500 cSt.

Figure 6. HFO kinematic Viscosity (cSt) values obtained by rheometer versus predicted by PLS models at different temperature ranges: (A) from 25 to 120 °C – General PLS model (B) from 27 to 45 °C and (C) from 85 to 110 °C.


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Figure 6C shows the performance of the viscosity model developed with spectra taken at temperatures between 85 and 110 ºC. For this model, 269 spectra were used being 231 to fit the model and 38 reserved for the testing step. The region of the spectrum used to correlate with the viscosity in this temperature range was between 7502.0 and 5446.2 cm–1, using only the SNV normalisation of the spectrum as preprocessing. The correlation between the predicted and experimental data for the fit and test steps was 0.92 for both cases with a RMSE of 3.9 cSt and 3.7 cSt, respectively, close to experimental error of 2.93 cSt. For comparison purposes, Filgueiras et al.35 presented a PLS model for prediction of oil viscosity with R2 of 0.818 at crossvalidation and 0.781 at test step obtaining a RMSECV of 20 cSt and RMSEP of 27 cSt for samples at 40 ºC. These parameters attest the good performance of developed PLS model to predict kinematic viscosity of HFO in the range of 4 to 70 cSt, which correspond to viscosity typically found after centrifugation process at thermoelectric plants powered by HFO. Table 2 presents a summary with the ranges of the studied properties, spectral regions selected to each PLS model, as well, the type of pre-processing employed, number of latent variables or factors employed and RMSEP for all PLS models developed in this work. One can observe a good performance of PLS model to predict HFO temperature and density in the entire range of temperature investigated (25 to 120 °C) with a single model for each property. Regarding to water content and viscosity models, a better performance was obtained when the models were calibrated to specific temperature ranges, correspondent to the conditions typically found at thermoelectric power plants (25 to 45 °C at HFO reception and 85 to 110°C after centrifugation process). Aiming to overcome the limitation presented mainly by general PLS models for water content and 23 ACS Paragon Plus Environment


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kinematic viscosity, an approach employing artificial neural network (ANN) was also evaluated. All the ANN models were developed to predict the properties of interest over the entire temperature range (25 to 120 °C) with a single model for each property.


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Table 2. Results of PLS calibration and test models for HFO properties: temperature, density, kinematic viscosity and water content. Spectral region Latent Spectra Pre-processing R2* RMSECV (cm-1) Variables Models applied from 25 to 120 ° C – General Models

R2

RMSEP

6101.5-4597.6

SNV + 1st Derivative

9

0.99

2.1

0.99

1.7

0.8849 – 0.9733

5608.2-4455.9

SNV+ 1st Derivative

8

0.99

0.002

0.99

0.0017

Viscosity (cSt)

4.2 – 5058.4

5450-4597.6

1st Derivative

9

0.59

535

0.725

462

Water content (%)

0.08 – 2

5450 – 4597.6

2nd Derivative

9

0.94

0.12

0.96

0.11

HFO property

Property Range

Temperature (°C)

25 – 120

Density (g.cm-³)

Models applied up to 45 ° C Viscosity I (cSt)

500 – 2300

6102-4580


7

0.74

247

0.88

131

Water content I (%)

0.08 – 2

6101.9-4597.6

SNV

6

0.94

0.11

0.94

0.11

Models applied from 85 to 110 ° C Viscosity II (cSt)

15 – 65

7502-5446

SNV

7

0.92

3.9

0.92

3.7

Water content II (%)

0.08 – 2

6101.9-4597.6


10

0.97

0.075

0.97

0.07

* Coefficient of determination (R2) for cross-validation step. The other one corresponds to test step.



3.7

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Artificial neural network (ANN) models ANN-type feed-forward back-propagation models were developed with MLP

(multilayer perceptron) architecture. Models based on ANNs are considered within chemometrics as nonlinear multivariate calibration models that can improve the error of prediction and adjustment for properties with nonlinear behaviour, as viscosity in relation to temperature, for example45. Initially, it was necessary to reduce the size of the database, since the high number of spectral points provided by raw NIR spectra, for example, may cause a process of ANN memorising and/or over-fitting, which may hinder the predictive and generalisation power of the model. In this sense, analysis of the principal components (PCA) was performed for NIR spectra in the wavelength region equivalent to that previously defined for the PLS models. The results of this analysis show how the variance of the spectral data matrix is explained in function of the number of principal components (PCs)46,47. It was selected a minimum number of PCs to feed the ANNs to avoid an over parameterization, but enough to describe a minimum of 90% of the variance of the spectral data matrix. Typically, the scores of 2 to 5 PCs were employed depending on the property and spectra pre-treatment. HFO temperature was employed together with PCs as input data to improve the performance of HFO density and viscosity models (Table 3). It should be emphasized that for ANN models it was selected only one NIR spectrum for each experimental condition, while for PLS models 2-5 spectra were employed to represent each experimental condition. For ANN optimization two training algorithms were evaluated: Levenberg– Marquardt and Bayesian regularization. The first was chosen because presents good convergences with respect to ANN training using variables from infrared spectra as 26 ACS Paragon Plus Environment

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input data15,16,32,48. The Bayesian regularization algorithm arises from the need to avoid the possibility of overfitting, a common problem present in the back-propagation training process that causes poor generalization impairing the performance in the prediction stage of the model49,50. Hyperbolic tangent (tansig) and linear (purelin) as transfer function were employed, allowing nonlinearity between the data, since when using nonlinear transfer (or activation) functions, all neural networks becomes nonlinear51. Initially only one hidden layer was tested, aiming to reduce the number of parameters generated. The best configuration of the neural model obtained in each property are presented in Table 3. The definition of the neurons number and the training algorithm was made through the evaluation of the lowest RMSE obtained by each algorithm as a function of neurons number.

Table 3. Best configurations obtained for ANN employing Bayesian regularization as training algorithms. Property

Input data

Temperature 5 PCs (°C) Density 2 PCs + (g.cm-3) Temperature Water Content 5 PCs (%) Viscosity (cSt)

4 PCs + Temperature

%Variance Architecture Explained*

Transfer Functions

RMSEP 1.7

99.56 %

5.10.1

Tansig-purelin

92.26 %

3.8.1

Tansig-purelin 0.0009

99.03 %

5.10.1

Tansig-Tansig

0.05

99.36 %

5.5.1

Tansig-Tansig

55.4

* Percentual of variance explained by PCs employed as input data.



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Bayesian regularization algorithm obtained smaller errors than the Levenberg– Marquardt algorithm for all models, reaching a minimum RMSE with 10, 8, 8 and 5 neurons at hidden layer for prediction models of HFO temperature, density, water content and viscosity, respectively. The determination of the number of ANN layers and the number of neurons in each layer does not involve accurate calculation formulas and theory, but can be defined through continuous trial and error comparisons52. Furthermore, it is evident the linear and non-linear relationships in the properties, because to adjust the temperature and density models the linear transfer function used in the output layer provided better performance in the models. On the other hand, hyperbolic tangent function was used in the two layers for WC and viscosity models. The performance of ANNs at training and test steps is represented by predicted vs experimental data plot at Figure 7. One can note that ANNs obtained were able to adjust the training data set and to predict a fully independent data set with similar precision levels. For all ANNs developed in this work the determination coefficient (R2) values were above 0.99 for both steps. Comparing the results obtained by PLS (general models 25 to 120 °C) and ANNs for HFO temperature, density and water content, similar correlations and RMSEP values were obtained for HFO temperature models, while RMSEP values for density and WC decreased to a half, approximately, when predicted by ANNs. However, the viscosity prediction error was considerably decreased from 467 cSt when general PLS model was used to 55.4 cSt by ANNs in the same temperature range. These results reveal the ability of ANNs to describe the behaviour of non-linear variables like kinematic viscosity in function of temperature.


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Figure 7. Experimental values versus predicted by ANN of all HFO properties investigated in this work in the entire temperature range. Superscripts 1 and 2 refer to model training and model test steps, respectively. 29 ACS Paragon Plus Environment


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For comparison purposes, ANN and PLS (general and by temperature range) models were tested regarding their ability to predict the kinematic viscosity profile for two random HFO samples, which were not used at PLS and ANN calibration step, at different temperatures. The results of this simulation are presented in Figures 8A and B.

Figure 8.Comparison between PLS models and ANN to predict experimental values of kinematic viscosity of two HFO aleatory samples in the temperature range from 25 to 120 °C.

Figure 8 shows the superiority of the ANN approach to HFO viscosity prediction compared to the PLS models, more notably at low temperatures (up to 45 °C). The best performance of ANNs in relation to PLS regression can be attributed to its nonlinear character, since PLS regression is a linear adjustment approach to problems that occur, for example, in the HFO density model53. Chi-square test at 95% confidence level proves the excellent performance of ANN to precisely describe the behavior of HFO kinematic viscosity in the temperature range from 25 to 120 °C. Thus, the advantage of using ANN lies in its ability to model processes with linear and nonlinear behaviour and ability to solve problems of overlapping signals, usually caused by spectroscopic analysis of multicomponent samples, such as HFO53. 30 ACS Paragon Plus Environment

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4 Conclusions NIR spectroscopy combined with chemometric models (PLS and ANN) were applied for on-line monitoring and prediction of HFO properties like temperature, density, water content (WC) and kinematic viscosity at temperature range typically found at thermoelectric plants powered by HFO. This is the first time that this approach (NIR combined with PLS and ANN) was applied to monitor HFO properties in a wide temperature range (25 to 120 °C). For HFO temperature and density a single PLS model (for each property) was satisfactory applied to cover the entire temperature range, obtaining determination coefficients of 0.99 and 0.98 at test step and RMSEP of 1.7 °C and 0.0017 g.cm-3, respectively. PLS models adjusted to predict WC and viscosity at the same temperature range present a poor predictive capacity, more notably for viscosity predictions. A better performance of PLS models for these properties was obtained when the adjustment was performed with NIR spectra collected at specific temperature ranges: 25-45 °C, representing HFO samples at thermoelectric plant reception (low temperature area) and 85-110 °C, representing HFO samples after centrifugation process (high temperature area). When artificial neural network (ANN) using principal component and temperature as input data and Bayesian Regularization as training algorithm was applied, a considerable predictive increment was observed for all properties. Determination coefficients greater than 0.99 were obtained for ANN models covering the entire temperature range. The results proved that the NIR spectroscopy combined with chemometric models (PLS and ANN) is a powerful tool for HFO on-line monitoring. In the sequence, the developed models will be tested at real situations in the thermoelectric power plant Energética Suape II S.A. NIR probes will be installed in different points of interest enabling an on-line HFO monitoring. The results of this step will be presented in the next paper. 31 ACS Paragon Plus Environment


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Corresponding Author: Prof. Dr. Gustavo R. Borges Center for Studies on Colloidal Systems (NUESC)/Institute of Technology and Research (ITP), Av. Murilo Dantas, 300, Aracaju-SE, Brazil, CEP 49032-490, Fax: +55-79-32182190, Phone +55 79-32182157, , E-mails: [email protected], [email protected], [email protected].

ACKNOWLEDGEMENTS The authors thank Energética Suape II S.A. for the financial and materials support. This article comes from the project entitled “Development and application of heavy fuel oil recovery systems from oily water streams and implementation of an online monitoring system of HFO quality at thermoelectric plants applying near-infrared spectroscopy”, financed by Energética Suape II S.A. and linked to the Research and Development Program of the National Electric Energy Agency – ANEEL. The authors also thank ITP (Institute of Technology and Research), UNIT (Tiradentes University), CAPES (Coordination for the Improvement of Higher Education Personnel) - Finance Code 001, CNPq (National Council for Scientific and Technological Development) and FAPITEC/SE (Fundação de Apoio à Pesquisa e à Inovação Tecnológica do Estado de Sergipe).

Supporting Information Further information on samples characteristics and reproducibility of NIR spectrophotometer is available free of charge at http://pubs.acs.org


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Abstract Graphic

Treatment Treatment FT/NIR

FT/NIR

Internal Combustion Engines

Property: Temperature (°C) Density (g.cm-3) Water content (%) Viscosity (cSt)


Application of Near-Infrared for Online Monitoring of Heavy Fuel Oil at

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