Potassium salts activated lignin-based biochar as an effective adsorbent for malachite green adsorption.

Herein, a series of lignin-based porous carbons (LC) were prepared from sulfonated lignin through a simple and environmentally-friendly one-pot activated carbonization together with various potassium compounds as activators, and used for malachite green (MG) adsorption. The results showed that the prepared biochar, especially after K2CO3 activation, exhibited a honeycomb profile with large surface area (2107.6 m2/g) and high total pore volume (1.1591 cm3/g), having excellent efficiency for MG adsorption, and the biggest adsorption capacity was 2970.0 mg/g. The kinetic study together with thermodynamic analysis indicated that the adsorption of MG by LC-K2CO3 conformed to pseudo-second-order model and the adsorption process was spontaneous, feasible, and endothermic. Moreover, LC-K2CO3 also displayed good stability and selectivity, and can selective separate the cationic dye from binary-dye system. Furthermore, the adsorption mechanism proposed in this work manifested that the high-efficient MG adsorption by LC-K2CO3 was a result of multiple actions including hydrogen bonding, electrostatic attraction, π-π interaction and n-π interaction as well as physical absorption. The work not only provide a fundamental theory for dye removal from wastewater, but offered a new insight for lignin valorization.

Enhancement of sugar release from sugarcane bagasse through NaOH-catalyzed ethylene glycol pretreatment and water-soluble sulfonated lignin.

In this work, five different NaOH-catalyzed ethylene glycol (EG) pretreatments together with water-soluble sulfonated lignin (SL) were used for enhancing sugarcane bagasse (SCB) enzymatic digestion. The results showed that the coupling of NaOH and EG into a one-pot pretreatment (10%NaOH/EG) was more beneficial to improve SCB enzymatic hydrolysis than that of single 10%NaOH or EG pretreatment, or the two-step pretreatment of NaOH and EG in different sequence (10%NaOH+EG and EG + 10%NaOH, respectively). The highest glucose yield of this work was 91.2 %, mainly released from the SCB that pretreated with 10%NaOH/EG at 130 °C for 60 min and 72 h enzymatic hydrolysis. The adding of SL into the enzymatic hydrolysis step could significantly lower the cellulase dosage and hydrolysis time from 20 FPU/g and 72 h to 10 FPU/g and 24 h, respectively, meanwhile keeping a high glucose yield of 90.4 %. The characterization of various pretreated or un-pretreated SCB confirmed that the improvement of hydrolysis efficiency of SCB after 10%NaOH/EG pretreatment was closely related to the removal of various components barriers in SCB and the fragmentation of pretreated solid. It can be concluded that the developed NaOH-catalyzed ethylene glycol pretreatment was an efficiency way to enhance the sugar release from SCB.

Comparison of acid and alkali catalyzed ethylene glycol organosolv pretreatment for sugar production from bagasse.

In this study, five acid or alkali catalyzed ethylene glycol (EG) organosolv pretreatments were proposed and compared for sugar production from bagasse. The results showed that compared with single EG/H2O pretreatment, the EG/H2O-HCl pretreatment was more efficient for both hemicellulose (~99.3%) and lignin (~67.1%) remove due to the synergistic effect of HCl and EG. The EG/H2O-NaOH pretreatment was also beneficial for lignin remove (~90.9%), but it was weak for hemicellulose degradation (~28.8%). Both EG/H2O-HCl and EG/H2O-NaOH pretreatments have good capacity to reserve the cellulose in pretreated solids. Following enzymatic saccharification, the largest glucose recovery yield from EG/H2O-HCl pretreatment was 94.3%, a slightly higher than this from EG/H2O-NaOH pretreatment (92.5%). However, its xylose recovery yield was only 77.3%, significantly lower than that of EG/H2O-NaOH pretreatment (93.5%). Besides, a certain amount of lignin also can be recovered from above acid or alkali catalyzed organosolv pretreatments through diluting or acidizing the pretreated liquids.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Trade-off performance analysis of LTI system with channel energy constraint.

In this paper, the trade-off performance between tracking error, control input energy and channel input power is studied. By modelling the communication channel as the additive coloured Gaussian noise channel (ACGN) with limited bandwidth, a new performance index is proposed and minimized over all stabilizing two-degree-of-freedom controllers. The results show that the trade-off performance is correlated to the intrinsic characteristics of the plant, including the locations and directions of the unstable pole, non-minimum phase zero. However it is unrelated to the non-minimum phase zeros of filter because of the two-degree-of-freedom controller. We also demonstrated that ACGN may degenerate the tracking performance. Finally, a typical example is given to validate the theoretical results.

Visual tracking based on extreme learning machine and sparse representation.

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.