Xiaoyan Feng,Xuejin Xie,Yidong Zhang,Guang Yu,2,Chao Liu,2,Bin Li,2,3,*,Qiu Cui,2,3

1. Key Laboratory of Biofuels, Shandong Engineering Laboratory of Single Cell Oil, Qingdao Engineering Laboratory of Single Cell Oil, Qingdao Institute of Bioenergy and Bioprocess Technology,Chinese Academy of Sciences,Qingdao,Shandong Province,266101,China

2.Shandong Energy Institute,Qingdao,Shandong Province,266101,China

3.Dalian National Laboratory for Clean Energy,Dalian,Liaoning Province,116023,China

Abstract: In this study, multivariate analysis methods, including a principal component analysis (PCA) and partial least square (PLS) analysis, were applied to reveal the inner relationship of the key variables in the process of H2O2-assisted Na2CO3 (HSC) pretreatment of corn stover. A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables. The results showed that the Na2CO3 dosage and pretreatment temperature had a strong influence on lignin removal, whereas pulp refining instrument(PFI)refining and Na2CO3 dosage played positive roles in the final total sugar yield. Furthermore, it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover. In addition, a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time, and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance (particularly for the final total sugar yield), indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment. Therefore,multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.

Keywords: lignocellulose pretreatment; corn stover; Na2CO3; principle component analysis; partial least square analysis

1 lntroduction

In response to the depletion of fossil fuels and severe environmental pollution around the world, a new economic model of "bio-economy" has been drawing significant attention. This guiding model encourages the recovery and reuse of limited resources and renewable materials to achieve a balance between economic prosperity and ecological survival[1-2]. Most recently, China generated a plan to realize carbon neutrality by 2060. To achieve this goal, new renewable energy technologies, including integrated biorefineries (e.g., bioethanol), will be developed and applied, and efforts to achieve large-scale commercial application of cellulosic ethanol by 2025 in China are ongoing.

Lignocellulosic biomass, mainly composed of cellulose, hemicelluloses, and lignin, for conversion into bioenergy or bio-chemicals has received much attention owing to its sustainability and renewability[3-5].However, to convert lignocellulosic biomass into fermentable sugars and further into biofuels or biochemicals, breaking the natural recalcitrance of lignocellulose prior to saccharification is a significant challenge owing to the tight and highly ordered structure of cellulose and the complicated structures of the major components of lignocellulose[6]. Hence,effective pretreatment is highly needed as a key step to disrupt the natural recalcitrance of lignocellulose and improve the amenability of polysaccharides for successful conversion. To date, many technologies have been developed for lignocellulose pretreatment,and different pretreatment methods have a different effectiveness when applied to lignocellulose[7-9]. For instance, alkali pretreatment has been one of the most studied approaches because alkaline nucleophilic reagents can break the linkages between hemicelluloses and lignin, swell the fibers, and disassemble the lignocellulosic matrix[10-11].Among the different alkalibased pretreatment methods, sodium carbonate(Na2CO3) as a pretreatment agent has received less attention than other alkali chemicals (e.g., NaOH and KOH) due to its relatively weak alkalinity. A weaker alkalinity may result in reduced delignification[12].However, Na2CO3pretreatment has less degradation of cellulose and hemicelluloses compared to acid,hydrothermal,and NaOH pretreatments[13].

As an eco-friendly oxidative agent, hydrogen peroxide (H2O2) decomposes to generate the hydroperoxy anion (HOO-) under alkaline conditions,which is the main oxygen species used to depolymerize lignin (during H2O2delignification) by reacting with the quinone structures of lignin and the double bonds of the side chains in the lignin[14-15]. Therefore, H2O2promotes the fragmentation of lignin and increases its solubility, thus improving the removal of lignin. In our previous study, H2O2was used to promote lignin removal during Na2CO3pretreatment, and an H2O2-assisted sodium carbonate (HSC) pretreatment was established[16]. After HSC pretreatment and enzymatic saccharification, the final total sugar yield reached up to 79% based on the content of the polysaccharides in the starting material. In addition, it is believed that Na2CO3pretreatment will have less environmental impact and potentially higher economic feasibility because the cost of Na2CO3is lower than that of NaOH,and the chemical recovery of Na2CO3does not require causticization[17].

Furthermore, in lignocellulosic biomass pretreatment, both process variables (such as the reagent dosage, pretreatment temperature, liquid-tosolid (L/S) ratio, and pretreatment duration) and the properties of the starting material (such as the particle size (PS) and chemical composition) can impact the effectiveness of pretreatment[18]. In most cases, the pretreatment process is complicated and multivariate,and the variables used in the process are interrelated to different extents[19].In this case,the interactions among the variables in the pretreatment are as important as the variables themselves[20]. Therefore, process modeling would be a valuable tool for extracting the maximum benefit of a pretreatment operation, such as a deeper understanding of the relationship between the key variables in pretreatment, process optimization, and the reduction of production cost, particularly for largescale applications.

Notably, multivariate analysis methods, including principal component analysis (PCA) and partial least square (PLS) analysis are valuable tools for reducing the comprehensive dimension systems, which can summarize the primary factors of the entire dataset[21].PCA is a useful data compression method for reducing the number of dimensions without much loss of information, and is the most common approach to reduce a large number of original factors to a smaller number of principal components(latent vectors),which explain the maximum amount of variance in the entire dataset. As shown in Fig. 1(a), the first principal component (PC) is the direction of the greatest variability in the data, and the second is the direction orthogonal to the first axis. Each successive axis explained less variance than its predecessors. Thus, the use of a PCA is usually the first step of a multivariate analysis applied to the entire dataset for detecting outliers and obtaining an overall picture. In contrast to the PCA, PLS models the relationship between the independentXmatrix and dependentYmatrix.The PLS analysis was conducted to establish an orthogonal space structure and link these new vectors (Comp) to real factors(Fig.1(b))[22].

Fig.1 Basic principles for PCA(a)and PLS(b)

As reported, multivariate analysis (PCA and PLS)methods have been applied to the analysis or monitoring of complex operations in biorefinery fields,such as a quantitative characterization of the lignocellulosic biomass[23], a prediction of the enzymatic hydrolysis mechanism of starch[24], and a prediction of the glucose yield ofPopulustrees from enzymatic hydrolysis[25]. In previous studies,multivariate analysis was used to initially evaluate the alkaline sulfite pretreatment process and mechanical refining-assisted alkaline pretreatment of corn stover,and the key variables impacting the complete pretreatment and enzymatic saccharification were highlighted in the NaOH pretreatment process[19,26-27].However, these preliminary studies only included extremely limited sample quantities (less than 30 experiments), and notably, the variables of the raw material properties (e.g., the particle size and chemical composition) were not included; thus, understanding of the complete process of pretreatment remains limited.In addition, to the best of our knowledge, a test for predicting the effectiveness of a certain pretreatment system using the developed PLS model has yet to be reported. In fact, prediction tests are of crucial importance in practical applications because an established PLS model with a robust predictive capability can be potentially used to monitor or control the pretreatment process and stabilize production,based on the known material properties and process conditions in advance. In fact, to obtain more valuable analysis results and achieve a robust prediction of the developed model, the sample numbers with a reasonable experimental design should be as large as possible, and the variation of each parameter should be covered by the largest and reasonably varying range.

Therefore, in the present study, a large sample size(120 pretreatment experiments under different experimental conditions) was obtained for the calibration (model establishment) using multivariate analysis (including PCA and PLS) to comprehensively reveal the internal relationship of the key variables during the HSC pretreatment process of the corn stover(a cover stover was used as the starting material because it is one of the main types of agricultural waste worldwide). Furthermore, an additional 15 pretreatment experiments with various pretreatment conditions were conducted to establish a new and separate dataset for the prediction test to check the final predictive performance of the developed PLS model.The prediction test is of vital importance for the real application of the developed PLS model for the analysis, monitoring, and control of a complex biomass pretreatment process. Process modeling through a multivariate analysis can also be used in other pretreatment processes (e. g., hydrothermal pretreatment, deep eutectic solvent pretreatment) for the biorefinery of lignocellulose in future commercial applications.

2 Experimental

2.1 Raw material and chemicals

The mature corn stover, kindly provided from Pingdu,Shandong Province, China, was air-dried before use.The dried corn stover was cut with a grinder, collected,and separated into the desired particle sizes (i.e., >5,5-10, 10-20, and 20-80 meshes, respectively). The separated samples were stored in sealed plastic bags at ambient temperature for water balance.

Cellulase with an enzymatic activity of 60 FPU/mL(56 mg protein/mL) was supplied by Qingdao Vland Biotech, Inc. (China). Chemicals including Na2CO3,H2O2,citric acid,sodium citrate,and sodium azide were all of analytical grade and purchased from Sinopharm Chemical Reagent Co., Ltd. (China). All chemicals were used without further purification.

2.2 HSC pretreatment of corn stover

Corn stover with different particle sizes was used as the feedstock for HSC pretreatment, and the process was conducted in a cooking digester system (VRD-42SDA, China Pulp and Paper Research Institute Co., Ltd.,Beijing, China). The cooking system included four small cooking tubes (1 L each) and 50 g oven-dried corn stover with the desired dosages of Na2CO3and H2O2at various liquid-to-solid (L/S (5∶1-10∶1))ratios were mixed uniformly and placed in each small cooking tube. The corn stover was then pretreated at various temperatures (from 100℃ to 160℃) for a certain holding time (30-50 min). After pretreatment,the cooking tubes were immediately cooled with tap water.The pretreated corn stover samples were washed to neutralize the pH value in a 500 mesh nylon bag,and the washed samples were then collected and stored at 4℃for further analysis. Fig. 2(a) and Fig. 2(b) show images of the corn stover before and after HSC pretreatment.

2.3 Mechanical refining treatment

A pulp refining instrument (PFI) mill refining (PL11-00, Xianyang TEST Equipment Co., Ltd., China)treatment of HSC pretreated corn stover was carried out to further improve the enzymatic digestibility. The consistency of the substrates was 10 wt%, and 4000 revolutions were applied for refining treatment based on our previous study[28]. After PFI refining, the samples were collected and stored in a refrigerator at 4℃. Fig. 2(c) shows an image of HSC pretreated corn stover with PFI refining.

2.4 Dye adsorption

The dye adsorption method can be used to semiquantify the porosity of biomass substrates[28]. The dye used in this study was Congo red dissolved in distilled water at a concentration of 20 g/L. A mixture of 0.1 g(oven dried) sample of HSC pretreated corn stover and 1 mL Congo red was placed in a glass bottle. The amount of distilled water required was then added at a L/S ratio of 100∶1(w/w).The corn stover was dyed in an incubator at 50℃with a shaking speed of 90 r/min for 24 h.After dyeing, the samples were centrifuged to separate the supernatant and the substrate. The supernatant was diluted 25 times before testing with a UV-visible spectrophotometer (UV-752, Youke,Shanghai,China)at a wavelength of 492 nm for Congo red[29]. The amount of dye absorbed (dye adsorption value (DAV)) by corn stover was measured from the decrease in the concentration of the dye solution after dyeing and was calculated as milligrams of dye per gram of corn stover sample.

2.5 Enzymatic hydrolysis

To evaluate the effectiveness of HSC pretreatment, the pretreated corn stover was hydrolyzed under fixed conditions with a cellulase loading of 20 FPU/g-glucan(18 mg-protein/g-glucan) following the standard procedure developed by the National Renewable Energy Laboratory (NREL) (NREL/TP-510-42629,2008). The reactions were conducted in an incubator shaker at a solid consistency of 2 wt%at 120 r/min and 50℃for 48 h, using a sodium citrate buffer (pH value of 4.8) and sodium azide (0.02% based on the dry weight of the substrate). Upon completion, the hydrolysate was collected through filtration for further analysis. Each group of hydrolysis experiments was carried out in duplicate for sugar analyses.

2.6 Analysis methods

The chemical compositions of the raw and HSC pretreated corn stover were analyzed using the NREL method (NREL/TP-510-42618, 2008) with a standard two-step sulfuric acid hydrolysis. Acid and enzymatic hydrolysates were determined using a highperformance liquid chromatography (HPLC) system(Model 1200, Agilent, USA) equipped with a Bio-Rad Aminex HPX-87P column at 55℃. Ultra-pure water was used as the mobile phase with a flow rate of 0.6 mL/min. The concentration of monosaccharides and the loss of sugar destruction during acid hydrolysis were corrected using the calibration curves of standard sugars.

The calculations for solid recovery (R-solid), the recovery rates of glucan (R-glucan)/xylan (R-xylan),and the delignification rate (D-lignin) after HSC pretreatment, as well as the sugar yields of glucose (Yglucose)/xylose (Y-xylose) and the final total sugar(glucose + xylose) (Y-TS) after saccharification were carried out according to our previous study[30]. The detailed calculation equations(Eq.(1)-Eq.(7))are listed below, and other corresponding abbreviations of the variables are shown in Table 1.

whereM1is the mass of HSC pretreated sample(g),M2is the corresponding mass of raw corn stover (g),M3is the mass of glucose in hydrolyzate (g),M4is the corresponding mass of glucan in raw corn stover (g),M5is the mass of xylose in hydrolyzate (g), andM6is the corresponding mass of xylan in raw corn stover(g).W1is the glucan content of HSC pretreated sample(wt%,P-glucan),W2is the glucan content of raw corn stover (wt%,U-glucan),W3is the xylan content of pretreated sample (wt%,P-xylan),W4is the xylan content of raw corn stover (wt%,U-xylan),W5is the lignin content of pretreated sample (wt%,P-lignin),andW6is the lignin content of raw corn stover(wt%,Ulignin).

2.7 Statistical analysis

In this investigation, analyses of all data were conducted using the soft independent modeling of class analogy software package(SIMCA-P 15.0.2,Umetrics).

A multivariate data analysis, including PCA and PLS, was applied to reveal the inner relationship of the key variables during the entire HSC pretreatment process of corn stover. After all experiments and characterizations were conducted,all data including the variables of the properties of raw corn stover as well as the corresponding pretreatment conditions and the effectiveness of HSC pretreatment (Fig. 2(d)) were collected in a complete dataset for calibration (i.e.,model establishment). In the entire dataset, the variable of PFI for the samples with PFI refining was logically denoted as 1, whereas the variable of PFI for the samples without PFI refining was denoted as 0. First, a PCA was used to analyze the entire dataset and remove the outliers and variables that were basically constant.Based on the PCA, a PLS analysis was used to further illustrate the interrelationship between theXandYvariables, as well as to build a calibration model.Subsequently, an additional 15 pretreatment experiments were conducted to build a new separate dataset for conducting a predictability test of the developed PLS model.

3 Results and discussion

3.1 PCA analysis for the entire process

First, the entire dataset was analyzed using a PCA to obtain an overall picture. As seen in Table 2, the PCA yielded five principal components with a cumulativeR2value of 0.745 and a cumulativeQ2value of 0.484.TheR2value is the percentage of variance of the entire dataset for all variables (Xwith PCA andYwith PLS)that can be illuminated by the model. The closer theR2value is to 1,the better the model obtained.However,a large cumulativeR2value (close to 1) is insufficient.TheQ2value indicates how well a variable can be predicted for the model (Xwith PCA andYwith PLS),and is calculated through a cross-validation. Note that,unlike the cumulativeR2value,the cumulativeQ2value is not additive. A large cumulativeQ2value (>0.5)suggests a good predictability of the model[19,31]. As a result, the cumulativeQ2value (0.484) indicates that the accuracy of this primordial PCA model is insufficient for a prediction.

To further examine the cumulativeR2andQ2values for each variable in this PCA model, the overview plot ofXfor the initial PCA model is shown in Fig. 3(a),which indicates that the variables ofP-E (extractive content in HSC pretreated corn stover) andP-ash (ash content of HSC pretreated corn stover) cannot be predicted by the PCA model, probably because of their lower variation in the results obtained. Therefore, bothP-E andP-ash were screened out, but the variables of H2O2dosage andH-t (holding time at pretreatment temperature) with negativeQ2value were included for a further analysis because they were key variables of the pretreatment conditions.

In addition, the initial PCA model identified 15 outliers (15 experimental points) that were outside the 95% confidence limit; thus, they were also removed.Consequently, an updated PCA model including 24 variables and 105 data points was built, and all variables with variation ranges are listed in Table 1.The updated PCA model yielded five principal components with a cumulativeR2value of 0.796 and a cumulativeQ2value of 0.558 (the model structure is presented in Table 2). Namely, the updated PCA model can elaborate 79.6% of the variation of the entire dataset, and a large cumulativeQ2value (>0.5)indicates a good predictive capability of the PCA model.

A score scatter plot of the updated PCA model is shown in Fig.3(b).The score scatter plot of a PCA is a map of the observations (including all experimental data points), and shows the possible presence of outliers, as well as the groups and similarities of the observations in the data[20].As can be seen from Fig. 3(b),most of the data are within a 95%confidence limit,and the samples can be grouped according to the particle size of the raw corn stover. Fig. 3(c) shows an overview plot of the updated PCA model, and the cumulativeR2andQ2values for each variable can be found. It can be seen that most of the variables had largeR2values within the range of 0.59-0.97, andQ2values ranging from 0.45 to 0.95, indicating that most of the variables can be well modeled,despite the poorer modeling for the variables of Na2CO3and H2O2dosages, and L/S ratio than the others. Based on the PCA analysis, the developed PLS model was built to further investigate the relationship between key variables in the HSC pretreatment system.

Fig.3 (a)Overview plot of X for the initial PCA model,(b)score scatter plot of the updated PCA model(t[1]is the first principal component,t[2]is the second principal component,the legends are the PS of the raw corn stover),and(c)overview plot of X for the updated PCA model

3.2 Analysis of the PLS model

In practice, a solid recovery (R-solid), the recovery rates of glucan (R-glucan)/xylan (R-xylan), the delignification rate (D-lignin), and the final total sugar yield (Y-TS) are the top key variables reflecting the effectiveness of pretreatment. Hence, the PLS model was built to further investigate the effect of the properties (e. g.,PSand glucan, xylan, and lignin contents) of the raw materials and the pretreatment conditions (e. g., the Na2CO3and H2O2dosages,pretreatment temperature, L/S ratio, and pretreatment duration) (asXvariables) on pretreatment effectiveness(reflected by the factors such asR-solid,R-glucan,Dlignin, andY-TS, asYvariables), as well as the interrelationship amongXandYvariables. In this case,a total of 18 key variables were selected to build the PLS model, which included 13Xvariables and 5Yvariables (Table 1). As presented in Table 2, the PLS model yields six principal components with a cumulativeR2value of 0.857 forXvariables, a cumulativeR2value of 0.671 forYvariables, and a cumulativeQ2value of 0.584. Therefore, the PLS model can explain approximately 67%of the variations in theYvariables (i. e.,R-solid,R-glucan,R-xylan,D-lignin, andY-TS) and it has a good overall predictability with a highQ2value(58.4%>50%).

Fig. 4(a) depicts theYoverview plots of the PLS model,which show the cumulativeR2andQ2values for eachYvariable. It can be seen that allYvariables have a highR2value(0.60-0.75),suggesting that they can be well explained by this PLS model.For the prediction of theYvariables, theQ2values are within the range of 0.57-0.67, except forR-solid (0.42), indicating that the prediction forR-solid is not as good as the others in the PLS model. Fig. 4(b) shows the variable importance plot (VIP) of the PLS analysis, which shows the influence of allXvariables on allYvariables simultaneously.The error bars of the variables in Fig.4(b) are standard errors. VIP values of greater than 1 suggest importantX-variables for the PLS model.It can be seen that the first four dominant variables for the PLS model are the pretreatment temperature (T, VIP value of 1.48), PFI refining (PFI, VIP value of 1.31),Na2CO3dosage (VIP value of 1.15), and particle size(VIP value of 1.11). Hence, compared with the properties of raw corn stover, pretreatment parameters have a stronger influence on the effectiveness of the HSC pretreatment, based on the data obtained in this study. The specific impact of these variables on the effectiveness of HSC pretreatment in the PLS model can be explained through the following loading scatter plots(Fig.4(c)-Fig.4(e)).

The loading scatter plot displays the inner relationship between theXandYvariables. The further theXvariable is from the origin, the higher its influence on theYvariables. In addition, the variables near each other are positively correlated, whereas the variables opposite each other are negatively correlated.As shown in Fig. 4(c), the first two principal components of the loading scatter plot are all dominated by the pretreatment temperature, which has a strong positive relationship with lignin removal. This result is due to the fact that more lignin would be removed at a higher temperature of the HSC pretreatment. However, pretreatment temperature is negatively related to the recovery rates of the sugars(Rglucan andR-xylan)andR-solids because of the greater degradation of carbohydrates at a higher pretreatment temperature[32-33]. For example, when the dosages of Na2CO3and H2O2were 35 wt% and 20 wt%,respectively, L/S ratio was 9∶1, the holding time was 40 min, and the pretreatment temperature varied from 100℃to 140℃, theD-lignin increased from 58.3% to 79.0%, theR-solid decreased from 72.2% to 51.9%,Rglucan decreased from 91.2% to 77.8%, andR-xylan decreased from 71.2% to 56.3%. Therefore, the temperature of the HSC pretreatment should be well controlled to obtain a high lignin removal and simultaneously avoid an over-degradation of the carbohydrates. Random hydrolysis of cellulose under alkaline conditions occurs at a higher temperature(>140℃)[11]. Thus, it is better to maintain the temperature of the HSC pretreatment at no more than 140℃. Fig. 4(c) also shows that the particle size is positively related toR-glucan andR-xylan, indicating that the particle size of the raw material should not be too small to reduce the loss of carbohydrates and decrease the energy consumption for the milling of the raw material. Thus, 5 mesh would be a good particle size for corn stover for HSC pretreatment.

Fig.4 (a)Y overview plot,(b)variable importance plot,loading scatter plots of PLS model for(c)component 1 vs.component 2,(d)component 3 vs.component 4,and(e)component 5 vs.component 6;and the coefficients plots of PLS model for(f)R-solid,(g)R-glucan,(h)R-xylan,(i)D-lignin,and(j)Y-TS

Fig. 4(d) shows that the location of the PFI refining is far from the origin in the third principal component,indicating that PFI refining has a strong impact on the PLS model. As reported previously, PFI refining can increase the surface specific area (porosity) of the substrates and decrease the crystallinity of the cellulose, thus enhancing the enzyme accessibility to substrates[26]. Compared with the samples without PFI refining, the samples with PFI refining had higher sugar yields. Therefore, PFI refining was extremely close toY-TS (Fig. 4(d)).Taking the samples pretreated with 35 wt%Na2CO3and L/S ratio of 6∶1 at 130℃for 40 min as an example, theY-TS was 45% without PFI refining, whereas the correspondingY-TS was higher than 72% with PFI refining. Therefore, postmechanical refining after HSC pretreatment is necessary to further improve theY-TS. Moreover, as presented in Fig. 4(d), the Na2CO3dosage was positively correlated with theY-TS, indicating that the Na2CO3dosage could be properly increased to obtain a higherY-TS. In addition, Fig. 4(e) shows that the fifth and sixth principle components (fine tuning of the PLS model) are dominated by the pretreatment temperature and Na2CO3dosage,respectively.

Moreover, the PLS coefficient plots for eachYvariable are crucial, and the corresponding plots were studied carefully. In the coefficient plots, the larger the absolute value of a variable is,the greater its impact on theYvariable[27]. In Fig. 4(f)-Fig. 4(j), the regression coefficients are presented for the quantitative relationships between theXvariables and the givenYvariable.TheXvariables are sorted in descending order based on the coefficients. The error bars for each variable represent the corresponding standard errors.As depicted in Fig. 4(f)-Fig. 4(j), the pretreatment temperature is the most negative variable forR-solid,R-glucan, andR-xylan because a high pretreatment temperature could result in more degradation of the carbohydrates[34]. In addition, the Na2CO3dosage and pretreatment temperature had a positive impact onD-lignin. For theY-TS, both PFI refining and Na2CO3dosage were the most effective and positive variables.However, the extractives and ash contents in the raw materials, as well as the L/S ratio, have little influence onY-TS, based on the results of this study. Hence, the L/S ratio of HSC pretreatment could be lowered to 5∶1-6∶1 to reduce water usage and process cost.

Fig.5 Predictability of PLS model with 15 new samples:(a)R-solid,(b)R-glucan,(c)D-lignin,(d)Y-TS,and(e)R-xylan(the point noted with the ellipse is sample 4,the X values represent the predicted data,and the Y values represent the real results of the pretreatment experiments),and(f)the contribution plot for sample 4

3.3 Predictability for the PLS model

To check the predictability of the PLS model, an additional 15 experiments under various pretreatment conditions were conducted to build a new and separate dataset for prediction. Fig. 5 and Fig. 6 show the predictability of the PLS model for the new data.TheXvalues represent the predicted data, and theYvalues represent the real results of the pretreatment experiments. As shown in Fig. 5, the correlation coefficients (R2) forR-solid,R-glucan,D-lignin, andY-TS were 0.8302, 0.9207, 0.7297, and 0.8209,respectively, indicating that the developed PLS model has good predictability forR-solid,R-glucan,D-lignin,andY-TS.However,Fig.5(e)shows that the correlation coefficient (R2) forR-xylan is only 0.4016, indicating that the PLS model cannot accurately predictR-xylan.This is because one data point with sample 4 in the prediction dataset is far from the fitting line (Fig. 5(e)).To determine why sample 4 in the prediction dataset cannot be well predicted, we can further examine the contribution plot of sample 4. When clicking on the point of sample 4 in theYVar/YPred plot forR-Xylan(shown in Fig.5(e)),the contribution plot(Fig.5(f))for the point of sample 4 appears. The contribution plot shows the weighted difference between the data of the point (scaled and centered as the work set) and the average point (center of the model plane). By default,the weights are the coefficients of the variables. In the contribution plot, the dominant bars indicate which variables deviate most from the reference point (here,the average)and the sign of the bar(down=minus,and up=plus)indicates the direction in which the variables deviate. With process knowledge, the contribution plot can help determine the cause behind the problem point and the solutions for troubleshooting.As shown in Fig.5(f), the variable H2O2dosage was clearly dominant.When we checked the experimental data of sample 4 in the prediction dataset, it was found that the dosage of H2O2for sample 4 was 30 wt% , which was the maximum value of the variation range of H2O2dosage(0-30 wt%)in the PLS model for calibration(Table 1).This is the probable reason why sample 4 was not well predicted by the PLS model. To achieve a good prediction, theXvariables, including the raw material properties and pretreatment conditions of the new experiments (new data for prediction) must fall within the ranges of theXvariables for the calibration of the PLS model.Thus,sample 4 with a high dosage of H2O2was removed from the prediction dataset (in fact, to reduce the process cost, the dosage of H2O2should not be too high owing to the high cost of H2O2, and no more than 5 wt% in industrial applications). In this instance, the correlation coefficient (R2) forR-xylan was improved to 0.7142, and the correspondingR2values forR-solid,R-glucan,D-lignin, andY-TS were 0.8360, 0.9476, 0.6416, and 0.8196, respectively (Fig.6).These results indicate that the PLS model achieves a good predictive performance and can be applied for a prediction ofR-solid,R-glucan,R-xylan,D-lignin, andY-TS, which is also in agreement with the fact that the PLS model has a large cumulativeQ2value(0.584).

Fig.6 Predictability of PLS model without sample 4 in the prediction dataset:(a)R-solid,(b)R-glucan,(c)R-xylan,(d)D-lignin,and(e)Y-TS

In this case, when the properties of the raw material and the pretreatment process conditions are known, we can quickly predict the values ofR-solid,R-glucan,Rxylan, andD-lignin, and the final result ofY-TS, in particular, thus knowing in advance how well the effectiveness of pretreatment based on the developed PLS model. This is of significant importance for real production. By monitoring the properties of the feedstock with online meters, which can also be developed based on a multivariate analysis[35], the operators can adjust the key process variables (e.g.,pretreatment temperature and chemical dosage) in advance to minimize the process variations, thus obtaining the final total sugar yield with a steady and high sugar concentration(Fig.7).Process stability is an extremely important and common concern for many industrial productions (such as pulp and papermaking and biomass pretreatment processes)[36-38]. Moreover,the process operators can quickly find solutions for troubleshooting according to the model developed through a multivariate analysis for a given process system[39]. Of course, to ensure that the model operates well, extensive data should be collected and used for calibration, and repeated calibrations with newly added data (for instance, once per month) are also needed,particularly for real applications.

Fig.7 Schematic diagram of the concept for process control based on a multivariate analysis

4 Conclusions

In this study, the evaluation of the H2O2-assisted Na2CO3(HSC) pretreatment was analyzed using the principal component analysis (PCA) and partial least square (PLS) methods, and the predictability of the developed PLS model was investigated. The results revealed an inner relationship between the properties of the raw material, pretreatment condition parameters,and variables of the pretreatment effectiveness.Furthermore, it was found that HSC pretreatment conditions (e. g., pretreatment temperature and postmechanical refining) had a greater influence on the effectiveness of the HSC pretreatment compared with the properties of raw corn stover (such as the particle size) based on the data obtained. According to the multivariate analysis, by suitably increasing the Na2CO3dosage, lowering the pretreatment temperature(≤140℃),and appropriately reducing the liquid-to-solid ratio (to 5∶1-6∶1), a satisfactory HSC pretreatment could be achieved. In addition, a predictive capability test using additional HSC pretreatment experiments(new data) proved that the developed PLS model achieves a good predictive performance, particularly for the prediction of the solid recovery,recovery rate of glucan, and final total sugar yield (the corresponding correlation coefficients (R2) between the experimental results and the predicted data were all above 0.8).Therefore, a multivariate analysis can be potentially used for monitoring, optimizing, and even controlling the process of biomass pretreatment in future largescale applications.

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (No. 31870568),Shandong Provincial Natural Science Foundation for Distinguished Young Scholars (China) (No. ZR2019JQ 10), the Major Program of the Shandong Province Natural Science Foundation(No.ZR2018ZB0208),and the "Transformational Technologies for Clean Energy and Demonstration" Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA21060201).

§The first two authors contributed equally.