Optimization of Alkaline Extraction of Xylan-Based Hemicelluloses from Wheat Straws: Effects of Microwave, Ultrasound, and Freeze-Thaw Cycles

The alkaline extraction of hemicelluloses from a mixture of three varieties of wheat straw (containing 40.1% cellulose, 20.23% xylan, and 26.2% hemicellulose) was analyzed considering the following complementary pre-treatments: freeze-thaw cycles, microwaves, and ultrasounds. The two cycles freeze-thaw approach was selected based on simplicity and energy savings for further analysis and optimization. Experiments planned with Design Expert were performed. The regression model determined through the response surface methodology based on the severity factor (defined as a function of time and temperature) and alkali concentration as variables was then used to optimize the process in a multi-objective case considering the possibility of further use for pulping. To show the properties and chemical structure of the separated hemicelluloses, several analytical methods were used: high-performance chromatography (HPLC), Fourier-transformed infrared spectroscopy (FTIR), proton nuclear magnetic resonance spectroscopy (¹H-NMR), thermogravimetry and derivative thermogravimetry analysis (TG, DTG), and scanning electron microscopy (SEM). The verified experimental optimization result indicated the possibility of obtaining hemicelluloses material containing 3.40% glucan, 85.51% xylan, and 7.89% arabinan. The association of hot alkaline extraction with two freeze-thaw cycles allows the partial preservation of the hemicellulose polymeric structure.

Statistical Analysis and Optimization of the Brilliant Red HE-3B Dye Biosorption onto a Biosorbent Based on Residual Biomass

Using various techniques, natural polymers can be successfully used as a matrix to immobilize a residual microbial biomass in a form that is easy to handle, namely biosorbents, and which is capable of retaining chemical species from polluted aqueous media. The biosorption process of reactive Brilliant Red HE-3B dye on a new type of biosorbent, based on a residual microbial biomass of Saccharomyces pastorianus immobilized in sodium alginate, was studied using mathematical modeling of experimental data obtained under certain conditions. Different methods, such as computer-assisted statistical analysis, were applied, considering all independent and dependent variables involved in the reactive dye biosorption process. The optimal values achieved were compared, and the experimental data supported the possibility of using the immobilized residual biomass as a biosorbent for the studied reference dye. The results were sufficient to perform dye removals higher than 70-85% in an aqueous solution containing around 45-50 mg/L of reactive dye, and working with more than 20-22 g/L of prepared immobilized microbial biosorbent for more than 9.5-10 h. Furthermore, the proposed models agreed with the experimental data and permitted the prediction of the dye biosorption behavior in the experimental variation field of each independent variable.

An Experimental Study on the Hot Alkali Extraction of Xylan-Based Hemicelluloses from Wheat Straw and Corn Stalks and Optimization Methods

In this paper, we describe an experimental study on the hot alkali extraction of hemicelluloses from wheat straw and corn stalks, two of the most common lignocellulosic biomass constituents in Romania. The chemical compositions of the raw materials were determined analytically, and the relevant chemical components were cellulose, hemicelluloses, lignin, and ash. Using the response surface methodology, the optimum values of the hot alkaline extraction parameters, i.e., time, temperature, and NaOH concentration, were identified and experimentally validated. The physicochemical characterization of the isolated hemicelluloses was performed using HPLC, FTIR, TG, DTG, and 1H-NMR spectroscopy. The main hemicellulose components identified experimentally were xylan, arabinan, and glucan. The study emphasizes that both corn stalks and wheat straw are suitable as raw materials for hemicellulose extraction, highlighting the advantages of alkaline pretreatments and showing that optimization methods can further improve the process efficiency.

Differential evolution-based optimization of corn stalks black liquor decolorization using active carbon and TiO2/UV

In this work, the active carbon adsorption and TiO₂/UV decolorization of black liquor were studied through experimental analysis (planned using Design of Experiments), modelling and optimization (with Response Surface Method and Differential Evolution). The aim is to highlight the importance of optimization methods for increasing process efficiency. For active carbon adsorption, the considered process parameters were: quantity of active carbon, dilution, and contact time. For TiO₂ promoted photochemical decolorization the process parameters were: TiO₂ concentration, UV path length and irradiation time. The determined models had an R squared of 93.82% for active carbon adsorption and of 92.82% for TiO₂/UV decolorization. The optimization of active carbon resulted in an improvement from 83.08% (corresponding to 50 g/L quantity of active carbon, 30 min contact time and 200 dilution) to 100% (corresponding to multiple combinations). The optimization of TiO₂/UV decolorization indicated an increase of efficiency from 36.63% (corresponding to 1 g/L TiO₂ concentration, 60 min irradiation time and 5 cm UV path length) to 46.83% (corresponding to 0.4 g/L TiO₂ concentration, 59.99 min irradiation time and 2.85 cm UV path length). These results show that the experiments and the subsequent standard RSM optimization can be further improved, leading to better performance.

Neural networks and differential evolution algorithm applied for modelling the depollution process of some gaseous streams

The depollution of some gaseous streams containing n-hexane is studied by adsorption in a fixed bed column, under dynamic conditions, using granular activated carbon and two types of non-functionalized hypercross-linked polymeric resins. In order to model the process, a new neuro-evolutionary approach is proposed. It is a combination of a modified differential evolution (DE) with neural networks (NNs) and two local search algorithms, the global and local optimizers, working together to determine the optimal NN model. The main elements that characterize the applied variant of DE consist in using an opposition-based learning initialization, a simple self-adaptive procedure for the control parameters, and a modified mutation principle based on the fitness function as a criterion for reorganization. The results obtained prove that the proposed algorithm is able to determine a good model of the considered process, its performance being better than those of an available phenomenological model.

Genetic Algorithms and Neural Networks Based Optimization Applied to the Wastewater Decolorization by Photocatalytic Reaction

This paper proposes a genetic algorithm and a neural network based procedure to estimate the optimal conditions for a dyestuff wastewater treatment process consisting of a heterogeneous photocatalytic oxidation. A simulated dyestuff effluent containing the azo dye Reactive Black 5 is decolorized by a photocatalytic reaction using TiO2 P-25 as catalyst in the presence of Fe+3 and H2O2. A simple feed forward neural network with one hidden layer was projected and used to predict the evolution in time of the decolorization of this type of wastewater. The neural model was included in the optimization procedure solved with a simple genetic algorithm. The goal of the optimization is to calculate the optimal reaction conditions (illumination time and amounts of reagents) which assure an imposed value for the transmittance.