Optimization of the Biomethane Production Process by Anaerobic Digestion of Wheat Straw Using Chemical Pretreatments Coupled with Ultrasonic Disintegration

Influences of straw alkaline pretreatment on biogas production and digestate characteristics: artificial neural network and multivariate statistical techniques

Anaerobic digestion is one of the best options for producing valuable end products (biogas and biofertilizer). The aim of this study was to investigate the influences of thermoalkaline pretreatment of wheat straw on biogas production and digestate characteristics from codigestion with waste-activated sludge. Different alkaline conditions (NaOH, KOH and Na2CO3) and pretreatment durations (1, 3 and 5 h) were used for straw pretreatment. Batch anaerobic codigestion of sludge and pretreated straw was conducted under different pretreatment conditions. A feedforward neural network (FFNN) model, logistic model and statistical analysis were applied to the experimental data to predict biogas and investigate the significance and relationships among the variables. NaOH pretreatment for 5 h showed the best treatment conditions: biogas yield was 6.59 times higher than that without treatment. Moreover, the proportions of total solids, total volatile solids, chemical oxygen demand and microbial count removed reached 63.52%, 74.60%, 78.15% and 82.22%, respectively. The methane content was 67.50%, indicating that the biogas had a high quality. The thermoalkaline pretreatment significantly affected biogas production and digestate characteristics, allowing it to be used as a biofertilizer. Experimental data were successfully modelled for predicting biogas production using the applied models. The R2 values reached 0.985 and 0.999 for the logistic and FFNN models, respectively.

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.

Ultrasonic Disintegration to Improve Anaerobic Digestion of Microalgae with Hard Cell Walls-Scenedesmus sp. and Pinnularia sp

Microalgae are considered to be very promising feedstocks for biomethane production. It has been shown that the structure of microalgal cell walls can be highly detrimental to the anaerobic digestibility of biomass. Therefore, there is a real need to seek ways to eliminate this problem. The aim of the present study was to assess the effect of ultrasonic disintegration of Scenedesmus sp. and Pinnularia sp. microalgal biomass on the performance and energy efficiency of anaerobic digestion. The pretreatment was successful in significantly increasing dissolved COD and TOC in the system. The highest CH₄ yields were noted for Scenedesmus sp. sonicated for 150 s and 200 s, which produced 309 ± 13 cm³/gVS and 313 ± 15 cm³/gVS, respectively. The 50 s group performed the best in terms of net energy efficiency at 1.909 ± 0.20 Wh/gVS. Considerably poorer performance was noted for Pinnularia sp., with biomass yields and net energy gains peaking at CH₄ 250 ± 21 cm³/gVS and 0.943 ± 0.22 Wh/gVS, respectively. Notably, the latter value was inferior to even the non-pretreated biomass (which generated 1.394 ± 0.19 Wh/gVS).

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.

A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies

Anaerobic Digestion (AD) is a well-established process that is becoming increasingly popular, especially as a technology for organic waste treatment; the process produces biogas, which can be upgraded to biomethane, which can be used in the transport sector or injected into the natural gas grid. Considering the sensitivity of Anaerobic Digestion to several process parameters, mathematical modeling and numerical simulations can be useful to improve both design and control of the process. Therefore, several different modeling approaches were presented in the literature, aiming at providing suitable tools for the design and simulation of these systems. The purpose of this study is to analyze the recent advancements in the biomethane production from different points of view. Special attention is paid to the integration of this technology with additional renewable energy sources, such as solar, geothermal and wind, aimed at achieving a fully renewable biomethane production. In this case, auxiliary heat may be provided by solar thermal or geothermal energy, while wind or photovoltaic plants can provide auxiliary electricity. Recent advancements in plants design, biomethane production and mathematical modeling are shown in the paper, and the main challenges that these fields must face with are discussed. Considering the increasing interest of industries, public policy makers and researchers in this field, the efficiency and profitability such hybrid renewable solutions for biomethane production are expected to significantly improve in the next future, provided that suitable subsidies and funding policies are implemented to support their development.

Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.