KOH catalyzed straw pyrolytic carbon based on machine learning

Renewable Energy 130 (2019) 1216e1225

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

Prediction of combustion activation energy of NaOH/KOH catalyzed straw pyrolytic carbon based on machine learning Wen Jiang a, b, Xianjun Xing b, c, *, Xianwen Zhang b, c, Mengxing Mi b, c a

School of Food Science and Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China Advanced Energy Technology and Equipment Research Institute, Hefei University of Technology, Hefei, Anhui 230009, PR China c School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 May 2018 Received in revised form 6 August 2018 Accepted 28 August 2018 Available online 31 August 2018

Wheat straw, corn straw and sorghum straw were used as raw materials. KOH and NaOH were used as catalysts to prepare straw pyrolytic carbon (SPC) and the characteristics of combustion activation energy (AE) were analyzed by thermogravimetric analysis. The distributed modified Coats-Redfern integration method was used to compute the distributed AE. The predictive models of combustion AE based on Linear Regression (LR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were proposed and compared. The results showed the AE variation trend of three kinds of SPCNaOH, SPCKOH and SPCNa-KOH were basically the same and obviously decreased. In the LR model, degree value was 2 and R2 reached 0.8531. In the SVR model, the kernel function was Polynomial, C ¼ 3000, degree ¼ 4, coef0 ¼ 0.3 and R2 reached 0.9048. In the RFR model, the n_estimators value was 400 and R2 reached 0.9834. Compared with the LR and SVR model, the RFR model was more suitable for the AE prediction of alkalicatalyzed SPC. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Combustion activation energy Machine learning Linear regression Support vector regression Random forest regression

1. Introduction Biochar is the carbonaceous materials obtained by pyrolyzing of biomass under anaerobic or hypoxic conditions at relatively low temperature (generally lower than 700  C) [1]. It can be widely used in agriculture [2], as an environmentally friendly energy source [3] and other fields [4,5]. As a large agricultural country, China is rich in biomass resources, and the annual output of straw is nearly 7 billion tons. Wheat straw (WS) [6], corn straw (CS) [7] and sorghum straw (SS) [8] are abundant, and most of the stalks are not properly utilized [9]. By reusing straw to prepare biochar can reduce the environmental pollution caused by it [10], greatly

Abbreviations: SPC, straw pyrolytic carbon; AE, activation energy; LR, Linear regression; SVR, Support vector regression; RFR, Random forest regression; WS, W, Wheat straw; CS, C, Corn straw; SS, S, Sorghum straw; WSPC, Wheat straw pyrolytic carbon; CSPC, Corn straw pyrolytic carbon; SSPC, Sorghum straw pyrolytic carbon; SPCNaOH, NaOH catalyzed SPC; SPCKOH, KOH catalyzed SPC; SPCNa-KOH, NaOH-KOH catalyzed SPC; N e NaOH, K e KOH; MSE, Mean square error; R2, Correlation coefficient; MAE, Mean absolute error; EVC, The interpretative square difference; PTC, Program time consuming; TV, The trial value; P, The predicted value. * Corresponding author. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China. E-mail address: [email protected] (X. Xing). https://doi.org/10.1016/j.renene.2018.08.089 0960-1481/© 2018 Elsevier Ltd. All rights reserved.

improve the additional value of straw [11] and bring remarkable economic benefits [12]. Compared with transporting straw, the transportation of biochar had a lower cost [13]. KOH [14] and NaOH [15], as common catalysts, are widely used in the preparation of biochar and their composition were low [16]. A lot of research results show that KOH can change the carbonization process of biomass [17], reduce the pyrolytic temperature of biochar and significantly increase the yield of solid products of cellulose and lignin [18]. Na and K belong to the same main family and are adjacent to each other in the periodic table, so they are similar in nature [19]. Chen [20] et al. studied the combustion emission characteristics of pyrolysis oil and gas from raw sewage, sewage with KOH activation and sewage with ZnCl2 activation and found that the three kinds of sludge samples had the same pollutant release patterns following the combustion of corresponding pyrolysis oils and gases with oxygen-enriched air. Carrascull [21] found KNO3/ZrO2 and KOH/ZrO2 could be catalysts and found active in the catalytic soot combustion. Liu et al. [22] studied the effect of NaOH treatment on combustion performance of Xilinhaote lignite and found that treatment of lignite with NaOH could reduce the reactivity of lignite at proper concentration. Karacan [23] et al. studied changes in thermal properties of crude oil asphaltenes induced by NaOH treatment and TG/DTG analysis

W. Jiang et al. / Renewable Energy 130 (2019) 1216e1225

showed that when the NaOH concentration was increased, maximum decomposition temperature of the molecule shifts to higher temperatures and asphaltene became less resistant to heat. Therefore, it is of great significance to apply KOH and NaOH to the preparation of biochar. However, the calculation of combustion activation energy in biochar combustion is more complicated and time-consuming. It has important research value to predict the combustion activation energy of biochar in some way if possible. In recent years, machine learning [24,25] has attracted people's attention with the development of computational algorithms and has played an important role in artificial intelligence, computer science, chemistry and biomedicine. Linear Regression, Support Vector Regression and Random Forest Regression were the three common prediction methods of machine learning. Linear Regression (LR) [26,27] is a statistical analysis method which is widely used to determine the quantitative relationship between two or more variables, using the Regression analysis in mathematical statistics. Pino-Mejías [28] et al. used LR models in predicting energy consumption and CO2 emissions cases offering ECM values closer to 0, with an R2 coefficient above 99%. Pilotorodríguez [29] et al. used multiple linear regression to predict the cetane number of biodiesel using. The model obtained was compared with two other models from literature and it was able to predict cetane number with 89% of accuracy, observing one outlier. The basic idea of Support Vector Machine (SVM) is to map the linearly indivisible and low dimensional spatial data into a linearly separable high-dimensional space through a nonlinear mapping and do regression and classification in this space. SVM, which is used to deal with regression problems, is called support vector regression (SVR) [30,31]. Yousefi [32] et al. used SVR methodology to predict output energy in rice production. The results showed that the proposed model improved the predictive accuracy and capa€ mer [33] et al. used SVR to estimate bility of generalization. Kro harvestable solar energy from Atmospheric Pressure. The trained model is used to estimate next day solar energy availability from a time series of recent atmospheric pressure values and their differences. Random forests are an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. The Random Forest Regression (RFR) [34,35] model has the advantages of high prediction accuracy, strong generalization ability, good n [36] et al. used robustness and few adjustment parameters. Hede Random Forest to predict hourly residential energy consumption to analysis the impact of household clustering on the performance accuracy. Results indicated that using Random Forest with K ¼ 32 clusters yield the most accurate results in terms of the coefficient of variation. Vetterli [37] et al. used Regression Trees to estimate energy expenditure in Children from raw accelerometer data. The results showed that Regression Trees precisely estimated the energy expenditure of cycling, riding a scooter, jumping and running. At present, there are few studies on the characteristics of combustion activation energy of SPC catalyzed by KOH, NaOH and mixed alkali and the AE prediction model [38,39] of alkalicatalyzed SPC based on machine learning has not been found. In this paper, wheat straw, corn straw and sorghum straw were used as raw materials. KOH and NaOH were used as catalysts to prepare SPC and the characteristics of combustion AE were analyzed by thermogravimetric analysis. The distributed modified CoatsRedfern integration method was used to compute the distributed activation energy. The prediction models of combustion AE based on LR, SVR and RFR were proposed and compared.

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2. Material and methods 2.1. Preparation of straw powder The tested wheat straw (WS), corn straw (CS) and sorghum straw (SS) were purchased from Hefei, Anhui, China. After washing with deionized water, the straw was dried for 12 h at 105  C in a thermostat oven until the quality is constant. The samples were grinded in the pulverizer for about 3e5min. Screening out uniform samples by vibrating screen machine. The straw was ground to a size of 50e80 meshes (0.18e0.3 mm) to achieve uniformity in the experiments. The sieve product was put into a sealed pocket which was placed into a dryer for storage. The elemental analysis of straw was shown in Table 1. It could be seen that there was little difference in the composition of three kinds of straw. 2.2. Preparation of SPC [40] The ratio of KOH and straw was set to 1:1, 2:1 and 3:1 (mass ratio). The ratio of NaOH and straw was set as 0.5:1, 1:1, 1.5:1 and 2:1 (mass ratio). The ratio of NaOH: KOH: the raw material was set to 1:1:1, 1:2:3 and 2:1:3 (mass ratio). Quantified KOH, NaOH determined by proportions and 5 g straw powder were mixed in the pulverizer for about 5e10 min until the mix was looking thoroughly homogeneous. Put the samples into the crucible which is made of corundum with a size of 113*30 mm and placed it the suitable position in the tube furnace and plug the furnace plug. To facilitate calculation and processing, the related mass ratio was converted to mass percentage. It contained mass percentage of straw, NaOH and KOH used to prepare SPC and the sum was 100%. NaOH and KOH were abbreviated as N and K. WS was abbreviated as W, CS was abbreviated as C and SS was abbreviated as S. For example, WSPCNAOH1-1 was recorded as W50N50K0 and so on. Three samples were configured to verify the combustion experiment model. For WSPC, they were W45N45K10, W15N80K5 and W25N10K65. CSPC and SSPC were the same. SPC preparation experiments were carried out in a high carbonization temperature tube furnace (gsl-1700, Hefei Kejing Material Technology co., LTD.). The pipe was made of corundum material with a size of 60*1000 mm. The air in the furnace tube was being displaced by streaming nitrogen into the tube with a flowrate of 60 mL/min for 20 min. The temperature was raised at a rate of 8  C/min to 600  C and hold at that temperature for 30 min. After the reaction was completed, the nitrogen atmosphere was kept until room temperature was reached. Then the samples were collected from the tube and sealed away in the dryer. The biochar was washed several times with 0.1 mol$L1 hydrochloric acid, followed by a deionized water wash until it turned neutral. After drying the sample were collected and sealed. Wheat straw pyrolytic carbon was marked as WSPC, corn straw pyrolytic carbon was marked as CSPC, sorghum straw pyrolytic carbon was marked as SSPC. When the ratio of KOH and straw was set to 1:1, 2:1 and 3:1, the base catalyzed WSPC was abbreviated as WSPCKOH1-1, WSPCKOH1-1 and WSPCKOH3-1 respectively. So was NaOH. When the ratio of NaOH: KOH: the raw material was set to 1:1:1, 1:2:3 and 2:1:3, the base catalyzed WSPC was abbreviated as WSPCNa-KOH1-1, WSPCNa-KOH1-2 and WSPCNa-KOH2-1 respectively. CSPC and SSPC were the same. 2.3. Sample test and combustion scheme The combustion test was carried out in the SETSTS Evo thermogravimetric analyzer (French Cypriot company). The carrier gas

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flow was 60 mL/min, simulating the air atmosphere (N2:O2 ¼ 4:1). The sample was 10 ± 0.2 mg and non-isothermal method was used. Starting at room temperature, the final temperature was 1000  C and the heating rate was 40  C/min. In order to eliminate system error and buoyancy effect, a blank experiment was conducted before each experiment.

2.4. Calculation and prediction of AE The distributed modified Coats-Redfern integration method was used to compute the distributed AE. Based on the traditional modified Coats-Redfern integral method, the distributed activation energy theory (DEAM) was introduced. The definition of a (the conversion rate) was the percentage of burned out mass relative to the initial mass. The whole combustion process was divided into 10 sections according to the conversion rate, and the iteration formula was introduced. Using computer software Python to program, calculate the corresponding N and E values under each conversion rate. The correlation coefficient R2 in each stage was greater than 0.99, which indicated the feasibility of fitting the activation energy of combustion with the above kinetic model. Each prediction model had 100 training data sets and 30 validation data sets. For example, for WSPC, 10 groups of training samples were WSPCNAOH0.5-1, WSPCNAOH1-1, WSPCNAOH1.5-1, WSPCNAOH2-1, WSPCKOH1-1, WSPCKOH2-1, WSPCKOH3-1, WSPCNA-KOH21, WSPCNA-KOH1-1 and WSPCNA-KOH3-1. According to the conversion rate from 0.1 to 0.94, each group could get 10 activation energies for each sample. This made up 100 tests for WSPC. Three samples were used to test so there were 30 validation data sets. CSPC and SSPC were the same.

2.5. AE prediction model building process of alkali-catalyzed SPC Python was used to build the model. Fig. 1 was the flow chart of the programming. The AE prediction model building process of alkali-catalyzed SPC included: data preparation, data cleaning, model parameter optimization, model training, model testing and model validation. The input data and output data of the unified format training set after data cleaning were used for the model training. According to the input and output characteristics of the model, key parameters were set and adjusted. After the model was built, the test set data was used to test and verify the model. The input was the adding mass percentage of NaOH, KOH and straw used to prepare SPC and the sum of the mass percentage of three substances was 100%. The output was the corresponding combustion AE of each interval from the conversion rate 0.1 to 0.94. The conversion rate range was shown in Table 2. Mean square error (MSE), correlation coefficient (R2), mean absolute error (MAE), interpretative square difference (EVC) and program time consuming (PTC) were used to evaluate the model. R2 was the correlation coefficient between the predicted and calculated AE of combustion. Three kinds of AE prediction model were established for WSPC, CSPC and SSPC with different conditions of KOH, NaOH and mixed alkalis respectively.

Table 1 Ultimate analysis of straw.

WS CS SS

N (%)

C (%)

H (%)

S (%)

O (%)

0.72 1.08 0.93

38.54 40.04 41.28

9.039 5.866 5.893

0.381 0.220 0.221

51.320 52.794 51.676

3. Results and discussion 3.1. Analysis of combustion activation energy of straw, SPC and NaOH catalyzed SPC Fig. 2(a), (b) and (c) showed that AE of WS, CS, SS, WSPC, CSPC and SSPC were significantly different. The activation energy of three kinds of SPC and SPCNaOH had the same change tendency. AE required for SPCNaOH was obviously decreased. With the increase of NaOH content, the curve of SPC slightly changed. With the increase of conversion rate, AE of WS, CS, SS gradually decreased and when the conversion was 0.6e0.7, its AE achieved the minimum. It slowly increased as the conversion rate continued to rise. There were two main reasons for the higher AE in the first stage: one was due to the partial pyrolysis of straw in the lowtemperature combustion stage to generate volatile matter and a small amount of charcoal. The reaction absorbed a large amount of heat which increased AE. The second was that the volatilization analysis was the main cause of the combustion in this stage. The activity of volatile molecules was low and the chemical reaction was relatively difficult [41]. The second stage of lower activation energy had three main reasons: one was mainly due to fixed carbon combustion in the high temperature combustion stage which had higher relative molecular activity. The reaction was more likely to occur. The second was due to preheating in the pre-stage reaction. Thirdly, due to the pyrolysis of the previous stage, the carbon was generated with a porous structure which was beneficial to aggravate the combustion reaction, so the activation energy is reduced at this stage [42]. The third stage of AE increased again because the combustibles had been basically burned and the remainder was mostly stable inorganic salts. The AE variation trend of three kinds of SPC was basically the same. The AE change curves of SPC and straw were obviously different. The reason was that straw was a biomass with high volatile and low fixed carbon content. SPC is the pyrolytic carbon after high temperature carbonization. Most of the volatiles had been removed. The components of SPC were mainly fixed carbon and the remaining flammable components were not much. Thus, its initial activation energy was higher than that of straw. AE increased when the conversion rate gradually increased. To a conversion rate between 0.3 and 0.6, AE did not change significantly because it was mainly coke combustion at this stage. When the conversion rate continued to increase, the AE increased gradually because the proportion of combustible components was getting lower and lower and its activation energy increases rapidly. The overall trend was inverse S-shaped. With the NaOH addition amount gradually increasing, the AE curve gradually began to deformation, to the downward offset. When the NaOH addition amount was more than 1.5, AE was slightly elevated. The AE curve of SPCNaOHaverage compared to the SPC, unless a was 0.06e0.1, showed AE for three kinds of SPCNaOH decreased by more than 15%. Wherein a was 0.1e0.2 the AE decreased amplitude was the maximum. WSPCNaOH decreased by 64.64%, CSPCNaOH decreased by 71.82%, and SSPCNaOH decreased by 85.92%. It could be seen that NaOH had the greatest influence on SSPC activation energy, followed by WSPC and CSPC. AE was influenced by the competition between organic matter and mean pore diameter. When a was 0.1e0.2, AE decreased with the increase of NaOH concentration compared to straw and SPC. This was because more and more amount of organic matter was removed from the straw with the increase of NaOH concentration. Berkowitz [43] et al. reported that at sufficiently high temperature only if sufficient oxygen could reach the particle surface, ignition and burning of the residual solid particle itself. When a was 0.2e0.96, AE reduced which was attributed to the bigger mean pore

W. Jiang et al. / Renewable Energy 130 (2019) 1216e1225

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Fig. 1. The flow chart of the programming.

Table 2 Conversion rate interval. I

II

III

IV

V

VI

VII

VIII

IX

X

0.06e0.1

0.1e0.2

0.2e0.3

0.3e0.4

0.4e0.5

0.5e0.6

0.6e0.7

0.7e0.8

0.8e0.9

0.9e0.94

diameter than that of straw and SPC. When the NaOH addition amount was more than 1.5, AE was slightly elevated maybe because of the excessive erosion of the carbon structure, the resulting pores were collapsed, interconnected and formed channels. 3.2. Analysis of combustion activation energy of straw, SPC and KOH catalyzed SPC Fig. 3(a), (b) and (c) showed that AE of three kinds of SPC and SPCKOH had the same change tendency. AE required for SPCKOH was obviously decreased. With the increase of KOH content, the curve of SPC did not change significantly. The AE curve of SPCKOHaverage compared to the SPC, unless a was 0.06e0.1, showed that AE of SPCKOH was decreased by more than 39%. Wherein a was 0.2e0.3, the AE decreased amplitude of three kinds of SPCKOH was the maximum. WSPCKOH decreased by 95.23%, CSPCKOH decreased by 91.24%, and SSPCKOH decreased by 85.94%. KOH had an average effect on three kinds of SPC, with the greatest influence on SSPC, followed by WSPC and CSPC. The trend of the three SPCKOH curves were basically the same which were all generally W-shaped. AE increased with the reaction process, when the conversion rate was over 0.3. When the conversion rate was over 0.8, the difference of AE with different KOH addition was not obvious. Compared with SPCNaOH, AE rising amplitude of SPCKOH was smaller, and the AE curve of SPCNaOH rose steeper. The chemical properties of NaOH and KOH were similar and the activation mechanism was basically consistent. So, compared with

straw and SPC, AE of SPCKOH had the same change trend as SPCNaOH. The mechanism of AE change of SPCKOH with different KOH con~ ero [44] et al. found that centration was like NaOH. Raymundo-Pin NaOH was only effective with disordered materials whereas KOH is effective whatever the structural order. After reaction of the poorly ordered precursor with KOH, the nanotubular morphology was completely destroyed, whereas it was preserved when NaOH was used. This was consistent with the result that compared with NaOH, KOH had a greater effect on the AE of SPC, and the decrease was more obvious. 3.3. Analysis of combustion activation energy of straw, SPC and NaOH-KOH catalyzed SPC Known from Fig. 4(a), (b) and 4(c), AE of three kinds of SPC and SPCNa-KOH had the same change tendency. AE required for SPCNa-KOH was obviously decreased. With the decreasing addition of NaOH and the increasing addition of KOH, AE of SPC began to decrease after moving in the direction of higher AE to a certain degree. AE of SPCNa- KOH1-2 was the lowest for three kinds of SPC, followed by SPCNa-KOH2-1 and SPCNa-KOH1-1. The trend of the three kinds of SPCNA-KOH curve were basically the same which were all generally W-shaped. When the conversion rate changed from 0.06 to 0.2, AE gradually decreased to the lowest level between 0.1 and 0.2. An increasing a led to a continuously rising AE. The AE curve of SPCNa-KOHaverage compared to SPC, unless a was 0.06e0.1, showed that WSPCNa-KOH, CSPCNa-KOH and SSPCNa-KOH

Fig. 2. 2(a) The activation energy curves of WS, WSPC, WSPCNaOH; 2(b) The activation energy curves of CS, CSPC, CSPCNaOH; 2(c) The activation energy curves of SS, SSPC, SSPCNaOH。.

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Fig. 3. 3(a) The activation energy curves of WS, WSPC, WSPCKOH; 3(b) The activation energy curves of CS, CSPC, CSPCKOH; 3(c) The activation energy curves of SS, SSPC, SSPCKOH.

Fig. 4. 4(a)The activation energy curves of WS, WSPC, WSPCNa-KOH; 4(b)The activation energy curves of CS, CSPC, CSPCNa-KOH; 4(c)The activation energy curves of SS, SSPC, SSSPCNaKOH.

were decreased by more than 33%. Mixed bases had an average effect on three kinds of SPC, with the greatest influence on WSPC, followed by CSPC and SSPC. It could be seen that NaOH, KOH and mixed alkali showed similar characteristics which could significantly reduce the burning AE. However, the effect of mixed alkali on CSPC was significantly stronger than that of adding KOH and NaOH separately. The effects of NaOH and KOH on WSPC and SSPC were approximately the same which were slightly stronger than CSPC, and the mixed alkali had the smallest difference in effecting three kinds of SPC. AE was the minimal when KOH was added separately. For mixed alkali it was slightly elevated, but the addition was significantly smaller than the addition of KOH alone. AE of SPCNaOH was the largest. The differences found between KOH and NaOH during activation are related with an additional intercalation step of metallic K or Na

produced during the redox reactions [44]. It is shown that metallic K had the ability to be intercalated in all materials in contrast with Na which could only intercalate in the very disorganized ones. The results indicated that if mixed alkali were added, the two produced a certain synergistic effect. Girginov [45] et al. pointed out the melting point of nearly equimolar eutectic of NaOH and KOH is 185  C, lower than the melting point of single NaOH (318  C). The use of equimolar KOHNaOH can reduce the melting temperature of the alkali during the heating process. In the process of preparing SPC by mixed alkali, the reaction rate and speed of the two alkalis were different. The consumption rate of alkali was different. So, when straw was added with mixed alkali, it showed different characteristics from adding NaOH and KOH separately in the process of preparing SPC.

Table 3 The results of non-repeated two-factor analysis. P < 0.001 The added amount of NaOH The added amount of KOH

0.001 < P < 0.01

0.01
P>0.05

II, III, IV

I, V, VI

VII, VIII, IX, X I, II, III, IV, VIII, X I, II, III, IV I, II, III, IV, V,VI, VII,VIII, IX, X I, II, III, IV, V, VI II, III, IV

VI

V, VII, IX

The ratio of NaOH-KOH

V, VI, VII, VIII, IX, X

Straw types when adding NaOH

Straw types when adding KOH

X

VII, VIII, IX

Straw types when adding NaOH-KOH

V, VI, VII, VIII, IX, X

I

W. Jiang et al. / Renewable Energy 130 (2019) 1216e1225 Table 4(a) Evaluation index of AE prediction model.

Table 4(c) Comparison of evaluation indexes of the test sets of the CSPC LR prediction model.

Degree

MSE

R2

MAE

EVC

PTC(s)

2

32.0871

0.8531

5.4111

0.8690

0.0130

Table 4(b) Comparison of evaluation indexes of the test sets of the WSPC LR prediction model.

MSE R2 MAE EVC

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W45N45K10

W15N80K5

W25N10K65

28.7304 0.8580 4.4652 0.8595

251.8250 0.0520 15.2352 0.9257

23.9341 0.8856 4.0587 0.9233

3.4. Interaction analysis of base catalyzed SPC AE at different conversion rates were used as an indicator. The added amount of NaOH and KOH, the ratio of NaOH-KOH and straw types were the main factors. The results of non-repeated two-factor analysis were shown in Table 3. Known from Table 3, the effects of NaOH, KOH and mixed alkali on SPC were different. It could be seen that KOH in the mixed bases played a leading role in the influence on AE and the mixed bases had a more continuous impact than the separate addition of KOH. The effect of NaOH was more extensive than that of adding KOH alone. KOH had a very significant impact on the range of 0.5e0.6. The effect of mixed alkali on AE was milder. When NaOH was added alone, the effect of different types of straw on AE was not significant. But the influence range of straw types when KOH and mixed alkalis were added was 0.6e0.94 and 0.06e0.1 and 0.4e0.94 respectively. It could be seen that compared to adding NaOH and KOH separately, the effect of straw species on AE when adding mixed alkali was different. The influence range was obviously enlarged and more extensive than that of KOH alone.

3.5. LR-based AE predictive model 3.5.1. Model training and parameter optimization of LR model The Pipeline mechanism was used to realize the flow-type encapsulation and management of multi-linear regression prediction model. First was the Z-score standard processing of input and output. The generated polynomial by using the PolynomialFeatures module was substituted into the LR model. The standardized data obtained in first step was used to train an effective prediction model. The detail about parameter settings of LR was uploaded in subchapter 1.1 of the appendix “Parameter optimization”.

3.5.2. Testing and validation of the LR model The training set was used to predict the AE of alkali catalytic SPC. With WSPC in the ratio of 50:33:17 as the test sample, the influence of Degree value on the prediction accuracy was studied by the above evaluation index. In order to determine the degree parameters, grid_searchcv was used to optimize the parameters. The degree range was [2,20] and the step length was 1. With the increase of degree, the correlation coefficient and generalization ability of the model decreased. So, the degree was determined as 2. The results of related indicator parameters were shown in Table 4(a). The LR model was weak in predicting the AE of WSPC and had insufficient generalization ability. The AE prediction models of CSPC and SSPC were like WSPC, so the Degree was set to 2.

MSE R2 MAE EVC

C45N45K10

C15N80K5

C25N10K65

40.3399 0.9076 5.3665 0.9172

95.6175 0.8243 8.4397 0.9551

24.9436 0.8961 4.3014 0.9024

Table 4(d) Comparison of evaluation indexes of the test sets of the SSPC LR prediction model.

MSE R2 MAE EVC

S45N45K10

S15N80K5

S25N10K65

57.5085 0.8694 6.1883 0.9196

780.2877 0.5097 27.3030 0.9236

21.3954 0.9164 4.3808 0.9219

3.5.3. Analysis of the results of AE prediction LR model of WSPC, CSPC and SSPC The evaluation index parameters of the test and real value were shown in Tables 4(b)e(d). The contrast between the trial value (TV) and the predicted value (PV) which changed with the conversion rate were shown in Fig. 5(a), (b)and (c). From Tables 4(b)e(d), the range of R2 in the AE prediction model of WSPC, CSPC, SSPC were 0.0520e0.8856, 0.8243e0.9076 and 0.5097-0.9164. It showed that the model had the best correlation with CSPC, and SSPC fluctuated most. The MSE range was 23.9391e251.8250, 24.9436e95.6175 and 21.3954e780.2877. The MAE range was 4.0587e4.4652, 4.3014e8.4397 and 4.3808e27.3030 respectively. It indicated that the error of AE prediction model was relatively large. The EVC range was 0.8595e0.9257, 0.9024e0.9172 and 0.9196e0.9236, indicating that the model was interpretable. 3.6. SVR-based AE predictive model 3.6.1. Data preparation and pre-processing of SVR model The data samples were randomly divided into 100 training sets and 30 test sets for the construction of the prediction model. It was necessary to normalize 100 sample data to [0,1] before the training to eliminate the influence of sample noise and drift and to improve the generalization ability of SVR model. 3.6.2. Model training and parameter optimization of SVR model To determine the degree parameters, grid_searchcv was used. The kernel function was Polynomial at C ¼ 3000, degree ¼ 4 and coef0 ¼ 0.3. The results of the related indicator parameters were shown in Table 5(a). The detail about parameter settings of SVR was uploaded in subchapter 1.2 of the appendix “Parameter optimization”. Compared to LR, MSE and MAE were decreased by 35.16% and 27.61%. R2 and EVC were increased by 6.06% and 5.57%. The SVR model had a stronger correlation with the AE prediction, and the generalization ability was better. But the model running time was significant growing. Building the model was time-consuming and the construction costs were raised. The AE prediction models of CSPC and SSPC were like WSPC. 3.6.3. Analysis of the results of the SVR model for WSPC, CSPC and SSPC AE prediction The evaluation index parameters of the test and real value were shown in Tables 5(b)e5(d). The contrast between TV and PV with the conversion rate were shown in Fig. 6(a), (b)and (c). Known from Tables 5(b)e(d), the range of R2 was

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Fig. 5. 5(a) Contrast between PV and TV of the test set of the WSPC LR prediction model; 5(b) Contrast between PV and TV of the test set of the CSPC LR prediction model; 5(c) Contrast between PV and TV of the test set of the SSPC LR prediction model.

Table 5(a) Evaluation index of SVM prediction model.

Table 5(c) Comparison of evaluation indexes of the test sets of the CSPC SVR prediction model.

MSE

R2

MAE

EVC

PTC(s)

20.8057

0.9048

3.9169

0.9174

0.7030

Table 5(b) Comparison of evaluation indexes of the test sets of the WSPC SVR prediction model.

MSE R2 MAE EVC

W45N45K10

W15N80K5

W25N10K65

15.0713 0.9255 3.0720 0.9287

26.6486 0.9000 5.0806 0.9968

18.6790 0.9109 3.5303 0.9554

0.9000e0.9255, all higher than 0.90. The positive correlation of PV and TV was relatively strong. The MSE range was 15.0713e50.5119. The MAE range was 3.0720e6.0607. It indicated that the error of the AE prediction model was relatively small. The EVC range was 0.9224e0.9968 indicating a strong interpretability of the model. In the three prediction models the mean R2 of CSPC was the best and WSPC was greater than SSPC indicating that the model had the best prediction fitness for CSPC. The mean MSE of SSPC was the best and CSPC was greater than WSPC. The mean MAE of SSPC was the best and CSPC was greater than WSPC. Compared to LR, the SVR model was obviously more suitable for the AE prediction of alkali-catalyzed SPC. MSE were decreased by 80.16%, 34.79% and 86.64%. MAE were decreased by 50.83%,15.02% and 86.64%. R2 were increased by 52.39%, 4.16% and 113.49%. EVC were increased by 6.37%,1.96% and 1.36%. 3.7. RFR-based AE predictive model 3.7.1. Data preparation and pre-processing of the RFR model Since the RFR model was insensitive to data normalization, no data normalization was required. Using the above training and test sample data, 4 groups of input and 1 group of output of the RFR AE prediction model were constructed. The regressor of RandomForestRegressor was called in the sklearn library. The detail about parameter settings of Randomforestregressor was uploaded in subchapter 1.3 of the appendix “Parameter optimization”. 3.7.2. The testing and validation of the RFR model Table 6(a) included the values of n_estimators as the default

MSE R2 MAE EVC

C45N45K10

C15N80K5

C25N10K65

41.8254 0.9043 6.0607 0.9773

41.1937 0.9243 5.9562 0.9293

21.8968 0.9088 3.3703 0.9224

Table 5(d) Comparison of evaluation indexes of the test sets of the SSPC SVR prediction model.

MSE R2 MAE EVC

S45N45K10

S15N80K5

S25N10K65

36.5272 0.9170 5.3860 0.9245

50.5119 0.9022 5.2789 0.9490

27.7814 0.9051 4.3049 0.9293

values (10), 100, 200, 400, 800, 1200, and 2000. Known from Table 6(a), when n_estimators value was less than 400. MSE, MAE and EVC all decreased with the increase of n_estimators and R2 significantly increased. From Table 6(a), when n_estimators value exceeded 400, MSE, MAE and EVC all increased with the increase of n_estimators and R2 significantly decreased. When n_estimators value reached 2000, MSE, MAE and EVC were reduced and R2 increased. But when n_estimators reached 2000, the program consumed time was higher than 1.8s, and the cost of the model building rose sharply. When it was lower than 400, the time was lower than 0.4s. Integrated the above two points, n_estimators was set to 400. The AE prediction models of CSPC and SSPC were like WSPC. Compared to LR and SVR, the RFR model was obviously more suitable for the AE prediction. MSE were decreased by 88.73% and 82.62%. MAE were decreased by 13.25% and 8.69%. R2 were increased by 70.47% and 59.20%. EVC were increased by 11.84% and 7.44%. 3.7.3. Analysis of the results of the RFR model for WSPC, CSPC and SSPC AE prediction The evaluation index parameters of the TV and PV were shown in Tables 6(b)e(d). The contrast between TV and PV with the conversion rate were shown in Fig. 7(a)e(c). Tables 6(b)e6(d) showed a range of R2 from 0.9755 to 0.9899, all higher than 0.95. The positive correlation of PV with the TV was relatively strong. The MSE range was 3.7966e6.8497. The MAE range was 1.7345e2.4995. It indicated that the error of the AE

W. Jiang et al. / Renewable Energy 130 (2019) 1216e1225

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Fig. 6. 6(a) Contrast between PV and TV of the test set of the WSPC SVR prediction model; 6(b) Contrast between PV and TV of the test set of the CSPC SVR prediction model; 6(c) Contrast between PV and TV of the test set of the SSPC SVR prediction model.

Table 6(a) Comparison of evaluation index of AE Prediction Model corresponding to different n_estimators. n_estimators

MSE

R2

MAE

EVC

PTC(s)

10 100 200 400 800 1200 2000

4.9867 4.2249 4.0842 3.6160 4.2033 4.9227 4.4778

0.9772 0.9806 0.9813 0.9834 0.9807 0.9774 0.9795

1.7864 1.7400 1.6484 1.5981 1.6695 1.8166 1.8025

0.9786 0.9842 0.9841 0.9857 0.9832 0.9806 0.9824

0.0160 0.0940 0.1790 0.3550 0.7110 1.3060 1.8131

suitable for the AE prediction of alkali-catalyzed SPC. In WSPC model, MSE were decreased by 95.16% and 75.58%. MAE were decreased by 75.03% and 49.22%. R2 were increased by 63.47% and 7.27%. EVC were increased by 8.46% and 1.97%. In CSPC model, MSE were decreased by 89.48% and 83.87%. MAE were decreased by 63.74% and 57.33%. R2 were increased by 12.52% and 8.03%. EVC were increased by 6.59% and 4.54%. In SSPC model, MSE were decreased by 98.33% and 87.48%. MAE were decreased by 83.62% and 58.56%. R2 were increased by 132.07% and 8.70%. EVC were increased by 6.59% and 4.54%. 4. Conclusion

Table 6(b) Comparison of evaluation indexes of the test sets of the WSPC RFR prediction model.

MSE R2 MAE EVC

W45N45K10

W15N80K5

W25N10K65

4.2420 0.9790 1.7345 0.9805

6.4864 0.9755 2.4455 0.9762

4.0180 0.9808 1.7529 0.9809

Table 6(c) Comparison of evaluation indexes of the test sets of the CSPC RFR prediction model.

MSE R2 MAE EVC

C45N45K10

C15N80K5

C25N10K65

6.2809 0.9856 2.3199 0.9856

6.8497 0.9874 2.4995 0.9875

3.7966 0.9841 1.7469 0.9844

prediction model was small. The EVC range was 0.9762e0.9900 indicating strong interpretability of the model. In the three prediction models the mean R2 of SSPC was the best and CSPC was greater than WSPC, indicating that the model had the best prediction fitness for SSPC. The mean MSE of CSPC was the maximum and WSPC was larger than SSPC. The mean MAE of CSPC was the maximum and SSPC was larger than WSPC. Compared with LR and SVR, the RFR model was obviously more

Table 6(d) Comparison of evaluation indexes of the test sets of the SSPC RFR prediction model.

MSE R2 MAE EVC

S45N45K10

S15N80K5

S25N10K65

4.4252 0.9899 2.0669 0.9900

5.4379 0.9895 2.1802 0.9894

4.5107 0.9820 1.9559 0.9839

The AE of three kinds of SPC, SPCNaOH, SPCKOH and SPCNa-KOH had the same change tendency. The effects of NaOH, KOH and mixed alkalis on SPC were different. KOH in the mixed bases played a leading role in the influence on the AE. The prediction models based on LR, SVR and RFR showed different characteristics. The RFR model was obviously more suitable than LR and SVR for the AE prediction of alkali-catalyzed SPC. The LR model was weak in predicting the AE of SPC and had insufficient generalization ability. Degree ¼ 2 and its MSE, MAE, R2 and EVC was 32.0871, 5.4111, 0.8531 and 0.8690. For the SVR model, the kernel function was Polynomial, C ¼ 3000, degree ¼ 4 and coef0 ¼ 0.3. Its MSE, MAE, R2 and EVC was 20.8057, 3.9169, 0.9048 and 0.9174. As for the RFR model, n_estimators was set to 400. Its MSE, MAE, R2 and EVC was 3.6160, 1.5981, 0.9834 and 0.9857. Compared with LR and SVR, MSE were decreased by 88.73% and 82.62%. MAE were decreased by 13.25% and 8.69%. R2 were increased by 70.47% and 59.20%. EVC were increased by 11.84% and 7.44%. The result indicated it was feasible to apply machine learning to predict the combustion activation energy of NaOH/KOH catalyzed straw pyrolytic carbon. This would greatly simplify the calculation of combustion activation energy process, save computation time and reduce costs. It provided a certain research basis for the related researchers to continue to study NaOH/KOH catalyzed SPC and a new research direction to study the change of AE in the combustion process. The evaluation parameters of the RFR model showed it was very suitable for the prediction of combustion AE which was different from other scholars’ research fields. For instance, Lindner [46] et al. had applied it to simulate for promotion of solar energy diffusion in residential consumer market. Ellis [47] et al. used it to predict energy expenditure and type of physical activity from wrist and hip accelerometers. Therefore, it had some novelty and open a new research field for the application of RFR method.

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Fig. 7. 7(a) Contrast between PV and TV of the test set of the WSPC RFR prediction model; 7(b) Contrast between PV and TV of the test set of the CSPC RFR prediction model; 7(c) Contrast between PV and TV of the test set of the SSPC RFR prediction model.

Acknowledgements The authors gratefully acknowledge the support from the Municipal Colleges and Universities Innovation Ability Promotion Projects of Beijing Municipal Education Commission (J2014QTXM0204); The International Science and Technology Cooperation Project of Anhui Province (1403062015); The Science and Technology Project of Anhui Province (2013AKKG0398).

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Appendix A. Supplementary data [19]

Supplementary data related to this article can be found at https://doi.org/10.1016/j.renene.2018.08.089.

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