Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R 2) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R 2 of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model

Effect of microwave-assisted acid or alkali pretreatment on sugar release from dragon fruit foliage

Agriculture residues are a promising feedstock for value-added products from lignocellulosic waste. However, pretreatment of lignocellulosic materials is essential to facilitate enzymatic hydrolysis and improve sugar yield. The objective of this study is to evaluate the effect of acid or alkali during microwave-assisted pretreatment of dragon fruit foliage (DFF) that would make hydrolysis process more efficient. In the present study, distilled water and three chemicals were examined for their effects on releasing monomeric sugar during microwave treatment. Microwave-assisted pretreatment namely microwave-distilled water (M-H2O) (control); microwave-sulfuric acid (M-H2SO4); microwave-sodium hydroxide (M-NaOH); and microwave-sodium bicarbonate (M-NaHCO3) pretreatment were performed using 5% (w/v) of DFF as substrate at 800 watt microwave power for 5 minutes exposure time. Highest yield of monomeric sugar was found at 15.56 mg/g using M-NaOH pretreatment at 0.1N NaOH. For M-H2SO4 pretreatment, 0.1N H2SO4 produced 8.2 mg/g of monomeric sugar. Application of M-NaHCO3 pretreatment using 0.05N NaHCO3 solution released 6.45 mg/g of monomeric sugar. While, soaking DFF in distilled water and subjecting to microwave irradiation released 6.6 mg/g of monomeric sugar. Treatments with the lowest concentration (0.01 N) of the three chemicals released only small quantities of total monomeric sugars and less than that with distilled water. The changes in the physical structure of DFF prior to and after the microwave-assisted pretreatment are also reported

Microwave-assisted pretreatment of sago palm bark

Three types of microwave-assisted diluted solvents were employed using 0.1 N H2SO4 (MSA), 0.1 N NaOH (MSH), and 0.01 N NaHCO3 (MSB). These solvents were evaluated as possible pretreatment routes for sago palm bark (SPB) with their effects on the pretreated substrate. A variety of analyses, consisting of fiber analysis, energy dispersive X-ray spectroscopy (EDX), X-ray fluorescence spectrometer (XRF), X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and high-performance liquid chromatography (HPLC), were performed to understand the pretreatment effects on the chemical and physical characteristics of SPB and pretreatment liquor. The thermal analysis has revealed that higher hemicellulose degradation was also found in MSA pretreatment. In the analyses of the pretreatment liquid for the extracted monomeric sugar, a higher amount of glucose was found (9 mg/g) using MSH pretreatment and the highest xylose level was found (4 mg/g) using MSA pretreatment. The analysis of the formation of inhibitors has shown that acetic acid was only found in the MSH pretreatment

Microwave assisted pretreatment and enzymatic hydrolysis for sugar production from Sago palm bark

Sago palm bark (SPB) is lignocellulosic biomass feedstock and a by-product of starch industry in Malaysia. The complex structure of lignocellulosic materials makes it resistant to enzymatic hydrolysis. Current technologies including physical and chemical pretreatment methods result in relatively low sugar yields, severe reaction conditions and high processing costs. A green and low energy pretreatment process is proposed using microwave irradiation. SPB was subjected to microwave-assisted pretreatment to assess the effects of pretreatment using diluted acid and alkaline solvents on sago palm bark characteristics and inhibitor formation. The effects of microwave-assisted pretreatment parameters (operating conditions) was also evaluated on glucose and xylose yield via enzymatic hydrolysis. Additionally, an estimation model for glucose and xylose yield from the enzymatic hydrolysis of SPB based on microwave-assisted pretreatment conditions was developed. The microwave-assisted pretreatments utilized three solvents which are 0.1 N H2SO4 (MSA), 0.1 N NaOH (MSH), and 0.01 N NaHCO3 (MSB). The microwave-assisted methods were compared to conventional heating pretreatment. The experimental design was done using a response surface methodology (RSM) and Box Bekhen Design (BBD) was used to evaluate the main and interaction effects of the pretreatment parameters on glucose and xylose yield obtained after the enzymatic hydrolysis step. The pretreatment parameters ranged from 5-15% solid loading (SL), 5-15 minutes of exposure time (ET) and 80-800 W of microwave power (MP). The enzymatic hydrolysis was carried out using 24 FPU/g of cellulase, 2 UN/g of xylanase and 50 U/g of β-glucosidase. An estimation model for glucose and xylose yield from the enzymatic hydrolysis of SPB was developed by using artificial neural network (ANN) and particle swarm optimization (PSO). The above-mentioned artificial intelligent systems were combined to form a hybrid PSO–ANN model. The MSA pretreatment resulted in higher lignin and hemicellulose degradation giving more porous structure of SPB compared to microwave-assisted alkaline and conventional pretreatments. No degradation products such as furfural, acetic acid and HMF were found in MSA pretreatment liquor. Conversely, conventional pretreatment using 0.1 N H2SO4 produced 0.47 mg/ml of acetic acid. After the enzymatic hydrolysis steps, it is revealed that the microwave-assisted pretreatment methods resulted in a higher sugar yield than conventional pretreatment methods. The results also show that the pretreatment parameters played a crucial role in the trend of the glucose and xylose yield from enzymatic hydrolysis of SPB. The results of glucose and xylose yield from MSA pretreatment and enzymatic hydrolysis of SPB were selected to develop a hybrid PSO–ANN model. The hybrid PSO–ANN model showed a higher regression coefficient (R2) for the estimation and the experimental values of glucose and xylose at 0.9939 and 0.9479, respectively. Meanwhile, R2 values of the RSM model were only 0.8901 and 0.8439 for glucose and xylose, respectively. This study concluded that the SPB has the potentials to be developed as future fermentable sugars source and the microwave-assisted pretreatment would be a possible route to enhance the release of these sugars