BackgroundIonic liquid (IL) pretreatment is receiving significant attention as a potential process that enables fractionation of lignocellulosic biomass and produces high yields of fermentable sugars suitable for the production of renewable fuels. However, successful optimization and scale up of IL pretreatment involves challenges, such as high solids loading, biomass handling and transfer, washing of pretreated solids and formation of inhibitors, which are not addressed during the development stages at the small scale in a laboratory environment. As a first in the research community, the Joint BioEnergy Institute, in collaboration with the Advanced Biofuels Process Demonstration Unit, a Department of Energy funded facility that supports academic and industrial entities in scaling their novel biofuels enabling technologies, have performed benchmark studies to identify key challenges associated with IL pretreatment using 1-ethyl-3-methylimidazolium acetate and subsequent enzymatic saccharification beyond bench scale.ResultsUsing switchgrass as the model feedstock, we have successfully executed 600-fold, relative to the bench scale (6 L vs 0.01 L), scale-up of IL pretreatment at 15% (w/w) biomass loading. Results show that IL pretreatment at 15% biomass generates a product containing 87.5% of glucan, 42.6% of xylan and only 22.8% of lignin relative to the starting material. The pretreated biomass is efficiently converted into monosaccharides during subsequent enzymatic hydrolysis at 10% loading over a 150-fold scale of operations (1.5 L vs 0.01 L) with 99.8% fermentable sugar conversion. The yield of glucose and xylose in the liquid streams were 94.8% and 62.2%, respectively, and the hydrolysate generated contains high titers of fermentable sugars (62.1 g/L of glucose and 5.4 g/L cellobiose). The overall glucan and xylan balance from pretreatment and saccharification were 95.0% and 77.1%, respectively. Enzymatic inhibition by [C2mim][OAc] at high solids loadings requires further process optimization to obtain higher yields of fermentable sugars.ConclusionResults from this initial scale up evaluation indicate that the IL-based conversion technology can be effectively scaled to larger operations and the current study establishes the first scaling parameters for this conversion pathway but several issues must be addressed before a commercially viable technology can be realized, most notably reduction in water consumption and efficient IL recycle.

Detecting safety helmet wearing in surveillance videos is an essential task for safety management, compliance with regulations, and reducing the death rate from construction industry accidents. However, it is much challenged by some factors like interocclusion, scale variances, perspective distortion, small object detection, and the carrier recognition of safety helmet. Traditional image‐based methods suffer from them. This article proposes a new methodology for detecting safety helmet wearing, which makes use of convolutional neural network‐based face detection and bounding‐box regression for safety helmet detection. On the one hand, the method can help to recognize the carrier of the safety helmet and detect a multiscale and small safety helmet. On the other hand, deep transfer learning based on DenseNet is introduced and applied using two different strategies, namely, object feature extractor and fine‐tuning for safety helmet recognition. To further improve the recognition accuracy, the network model with two peer DenseNet networks is trained by mutual distillation. Extensive analysis and experiments show that the novel methodology has considerable advantages in detecting safety helmet wearing compared to other state‐of‐the‐art models. The proposed model has achieved 96.2% recall, 96.2% precision, and 94.47% average detection accuracy. These results, precision‐recall (PR) curve, and receiver operating characteristic (ROC) curve demonstrate the feasibility of the new model.

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In the production of poly­(vinyl alcohol), the raw materials methyl acetate (MeOAc) and methanol (MeOH) exist as a homogeneous azeotropic mixture. Twenty-five kinds of ionic liquids, composed of five types of cations and five types of anions, were studied using the COSMO-SAC method. The σ-profile data for each component and the selectivity at infinite dilution (S ∞) were calculated and analyzed, respectively. 1-Hexyl-3-methylimidazolium chloride ([HMIM]­[Cl]) and 1-butyl-3-methylimidazolium chloride ([BMIM]­[Cl]) were selected as suitable entrainers based on the COSMO-SAC method. The binary interaction parameters of the NRTL model of the MeOAc/ionic liquid and MeOH/ionic liquid systems were regressed. The conceptual design for the separation of MeOAc and MeOH using ionic liquids as entrainers was investigated. The comparison of two processes using two entrainers was carried out from an economic perspective. The total annual cost (TAC) of the process using [HMIM]­[Cl] as an entrainer can be reduced by 16.5% compared with that of the process using [BMIM]­[Cl]. The results indicated that the COSMO-SAC method is feasible for screening ionic liquids as optimal entrainers. This work could provide theoretical instruction for further industrial applications using ionic liquids as solvents via COSMO-SAC computer-aided screening.

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