Advancing two-stage hydrogen production from corn stover via dark fermentation: Contributions of thermally modified maifanite to microbial proliferation and pH self-regulation.

This study introduces a two-stage hydrogen production enhancement mechanism using natural particle additives, with a focus on the effects of thermally modified maifanite (TMM) and pH self-regulation on dark fermentation (DF). Initial single-factor experiments identified preliminary parameters for the addition of TMM, which were further optimized using a Box-Behnken design. The established optimal conditions which include mass of 5.5 g, particle size of 120 mesh, and temperature of 324 °C, resulted in a 28.7 % increase in cumulative hydrogen yield (CHY). During the primary hydrogen production stage, TMM significantly boosted the growth and activity of Clostridium_sensu_stricto_1, enhancing hydrogen output. Additionally, a pH self-regulating phenomenon was observed, capable of initiating secondary hydrogen production and further augmenting CHY. These findings presented a novel and efficient approach for optimizing biohydrogen production, offering significant implications for future research and application in sustainable energy technologies.

Enhancing dark fermentative hydrogen production from wheat straw through synergistic effects of active electric fields and enzymatic hydrolysis pretreatment.

Hydrogen, a clean and sustainable energy source, faces challenges from energy-intensive pre-processing technologies. This study explores the synergistic enhancement of active electric fields on enzymolysis of wheat straw and hydrogen production through dark fermentation. The active electric field enzymolysis system improved the adsorption capacity of wheat straw to cellulase, increasing cellulase activity by 18.0 %, causing a 39.1 % increase in reducing sugar content. In the active fermentation system, Clostridium_sensu_stricto_1 activity was enhanced in the first stage, increasing hydrogenase activity by 23.0 %, prolonging the first hydrogen production peak. Elevated reducing sugars were observed in the second stage, with Prevotella_9 and Bacteroides becoming the dominant hydrogen-producing bacteria in the third stage, leading to a second hydrogen production peak. Overall, cumulative hydrogen production was enhanced by 50.9 % compared to the control. The synergistic pretreatment with an active electric field and cellulase provides a novel approach for efficiently utilizing wheat straw.

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine.

In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.

Fixed-time time-varying output formation-containment control of heterogeneous general multi-agent systems.

This work investigates fixed-time time-varying output formation-containment control of heterogeneous general multi-agent systems, which comprises one virtual leader, multiple leaders, and followers. First, fixed-time nonlinear control law is constructed, such that leaders can achieve time-varying output formation in a finite time. Meanwhile, leaders can track the virtual leader's output trajectory. Next, two complete distributed adaptive fixed-time observers are designed aiming to recover a convex hull. This is key to control issue in this paper. Further, nonlinear control law is constructed for followers, such that followers can move into the convex hull formed by multiple leaders. Finally, two examples are provided to verify the feasibility of the theoretical results.

Thermally modified tourmaline enhances hydrogen production by influencing hydrolysis acidification in two stages during dark fermentation of corn stover.

This study investigated the influence of thermally modified tourmaline (Tur) on hydrogen production during the dark fermentation of corn stover. Single-factor experimental results revealed influencing factors of particle size, mass, and temperature. Optimization of the experimental process was achieved using the Box-Behnken design, reaching optimum at conditions of 407 °C, 910-mesh, and 6.2 g. The principle analysis experiment showed that the Tur-enhanced group (Tur_En) amplified cumulative hydrogen production by elevating hydrogen production during the sugar-production stage. The Tur_En group's cumulative hydrogen production was measured at 396.2 ± 40.3 (mL/g VS), marking a 34.2% increase compared to the control group. Analysis of microbial diversity indicated that Firmicutes and Bacteroidota emerged as dominant colonies in both stages. Tur facilitated hydrogen production by stimulating the activity of Firmicutes. This study suggests a highly effective Tur-enhanced technology for hydrogen production from corn stover and elucidates the principles underpinning this method from two stages.

Deep Learning Models for SO2 Distribution in a 30 MW Boiler via Computational Fluid Dynamics Simulation Data.

The distribution of SO2 in a boiler is an important factor affecting tube corrosion in a furnace. To investigate the correlation between SO2 distribution and numerous variables (e.g., temperature, O2 distribution, etc.), a hybrid deep learning model is developed via the computational fluid dynamics (CFD) simulation data. First, the combustion process under typical working conditions is simulated to output the training data set. Then, a LASSO algorithm is adopted to select input variables with a high correlation with SO2 distribution. Finally, a deep belief network combined with a restricted belief machine and a fully connected layer is developed to describe the nonlinear relationship. The proposed model is the first work to use a deep learning algorithm to obtain the correlation between SO2 distribution and other products of combustion. The results show that O2 concentration has the highest influence on SO2 distribution.

Tourmaline enhanced methane yield via regulating microbial metabolic balance during anaerobic co-digestion of corn stover and cow manure.

This work performed a pilot-scale study on the effects of the substrate ratio, the concentration of tourmaline (Tur), and its high-temperature thermally modified (HTM) material on the anaerobic co-digestion of corn stover (CS) and cow manure (CM). The experimental results showed that the CH4 yield was higher at a corn stover -to- cow manure feeding ratio of 2:1. The cumulative CH4 yield increased by 22.76% and 8.31% at a concentration of tourmaline of 2.0 g/L and a tourmaline treatment temperature of 400 °C respectively. Microbial diversity analysis revealed that adding low doses of tourmaline regulated the distribution of microorganisms and that Methanobacteria became the dominant methanogenic archaea in the fermentation broth. This work clarified the effect of the concentration of tourmaline on gas production by anaerobic co-digestion from the perspective of the microbial metabolic balance and suggested the possibility of its application on a larger scale.

[Mechanisms of the removal and remediation of phenanthrene and pyrene in soil by Pogonatherum paniceum].

The mechanisms of the removal and accumulation of phenanthrene (PHE) and pyrene (PYR) by rock plant Pogonatherum paniceum were studied by pot experiments. Results showed that P. paniceum might effectively remove PHE and PYR from soils at their initial concentrations of 20 to 322 mg x kg(-1). About 50.97%-86.77% of PHE or 46.45%-76.7% of PYR was removed from the soils respectively after 70-day plantation of P. paniceum; the average removal rates of PHE or PYR from the soils by P. paniceum were 63.56% and 58.6% higher than those of CK1 (with addition of 0.1% NaN3), and 46.09% and 42.92% higher than those of CK2 (without NaN3). P. paniceum did show ability to accumulate polycyclic aromatic hydrocarbons (PAHs) from the soils; the contents of PAHs in its root and shoot increased withthe increase of PAHs concentrations in the soils. The bioconcentration factors (BCFs) for PAHs tended to decrease with increasing concentrations of these contaminants in soil. BCFs for PYR were higher than those for PHE, and BCFs for PHE (0.12-1.63) and PYR (1.03-5.61) in shoots were much lower than those in roots (0.21-3.08 and 1.31-10.11) at the same treatment. The mechanisms of phytoremediation processes by P. paniceum involve both biotic and abiotic factors. Contributions of each factor to the phytoremediation processes were estimated, which indicated that abiotic loss, plant accumulation, phytodegredation and microbial degradation accounted for 5.1%, 0.32%, 4.22% and 17.47% of the total removal of PHE, and 2.56%, 4.27%, 2.01% and 15.68% of PYR from soils, respectively. In contrast, 41.56% removal of PHE and 36.64% of PYR were attributed to plant-microbial interactions. Thus plant-microbial interactions are the main mechanisms for the remediation of soil PAHs pollution.