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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.

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.

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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.

Numerous studies have been done on the corrosion process of different dental metallic materials, especially using a comparative method and analyzing the electrochemical phenomena. Simultaneously, the effects of the corrosion process have been quantify by different physical quantities, such as corrosion rate, corrosion resistance, polarization resistance, corrosion current density etc. These experimental data can be used to model the corrosion process and, subsequently, to perform predictions with the aim to analyze or to control the process. In this work, a series of experimental data about corrosion of some titanium-based dental materials in artificial saliva were obtained through electrochemical impedance spectroscopy (EIS) tests and used as a database to develop a model of corrosion process by artificial neural networks. The process parameters taken into account were chemical compositions of the materials (cp-Ti, NiTi, NiTiNb), immersion time, pH, NaF content, and albumin content. The corrosion resistance of the metallic materials was evaluated by polarization resistance determined by EIS tests. Neural networks were developed and applied for evaluating the corrosion resistance of the alloys, depending on the process parameters. The predictions provided by the model are useful to understand the contribution of each parameter in the process and possible ways to control it.

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.

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.