State of the art and research development prospects of energy and resource-efficient environmentally safe chemical process systems engineering

In this focus overview, the main types and directions of engineering, methods and techniques of intensification of chemical process systems (CPS) and process optimization of energy- and resource-efficient processes for the representative production of titanium compounds, mining waste processing systems, electrochemical coating technologies, combined technologies for the treatment of industrial effluents and energy-and resource-efficient technologies for cleaning soils from petroleum and chemical pollution products are reviewed. The following issues have been discussed: methods of complex assessment of production energy efficiency and software and information support for automated synthesis of optimal energy-efficient regenerative heat exchange systems using pinch analysis; methods and algorithms for fractal-statistical characteristics analysis of nonstationary gas flows in complex gas pipelines; methods of ecological and economic optimization of production, infrastructure supply chains; methods for assessing and preventing the dangerous environmental impact assessment of chemical pollution; organization and logistics management of business processes engineering for improving the energy efficiency of plants; engineering of problem oriented computer systems, heuristic-computational models and algorithms for intelligent integrated logistics support of the equipment life cycle; engineering developments in the field of digital transformation of energy-efficient CPS and technological production systems; application of methods for optimizing reliability factors optimization, digitalized risk and safety management in the engineering of energy- and resource efficient CPS.     

A Bait-and-Switch Method for the Construction of Artificial Esterases for Substrate-Selective Hydrolysis

Outcomes of chemical reactions are generally dominated by the intrinsic reactivities of reaction partners, but enzymes frequently override such constraints to transform less reactive molecules in the presence of more reactive ones. Despite the attractiveness of such catalysis, it is difficult to build synthetic catalysts with these features. Micellar imprinting is a powerful method to create template-complementary binding sites inside protein-sized water-soluble nanoparticles. When a photocleavable functional monomer was used to bind two phosphonate/phosphate templates as transition-state analogues, active sites with predetermined size and shape were formed inside doubly cross-linked micelles through molecular imprinting. Postmodification replaced the binding group with a catalytic pyridyl group, forming highly selective artificial esterases. The catalysts displayed enzyme-like kinetics and turnover numbers that were in the hundreds. The selectivity of the catalysts, derived from the substrate-complementary imprinted active sites, enabled transformation of less reactive esters in the presence of more reactive ones.     

Computational-Based Approach for Predicting Porosity of Electrospun Nanofiber Mats Using Response Surface Methodology and Artificial Neural Network Methods

Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

一种自适应人工蜂群算法求解U型顺序相依拆卸线平衡问题

产品拆卸过程中零部件之间会相互干扰影响任务作业时间,基于该情形构建了多目标U型SDDLBP优化模型,并提出一种自适应ABC算法。所提算法设计了自适应动态邻域搜索方法,以提高局部开发能力;采用了轮盘赌与锦标赛法结合的分段选择法,以有效评价并选择蜜源进行深度开发;建立了基于当前最优解的变异操作,以提高全局探索能力快速跳出局部最优。最后,通过算例测试和实例分析验证算法的高效性。     

A Near-Infrared-Controllable Artificial Metalloprotease Used for Degrading Amyloid-β Monomers and Aggregates

Proteolysis of amyloid-β (Aβ) is a promising approach against Alzheimer's disease. However, it is not feasible to employ natural hydrolases directly because of their cumbersome preparation and purification, poor stability, and hazardous immunogenicity. Therefore, artificial enzymes have been developed as potential alternatives to natural hydrolases. Since specific cleavage sites of Aβ are usually embedded inside the β-sheet structures that restrict access by artificial enzymes, this strongly hinders their efficiency for practical applications. Herein, we construct a NIR (near-IR) controllable artificial metalloprotease (MoS₂-Co) using a molybdenum disulfide nanosheet (MoS₂) and a cobalt complex of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (Codota). Evidenced by detailed experimental and theoretical studies, the NIR-enhanced MoS₂-Co can circumvent the restriction by simultaneously inhibition of β-sheet formation and destroying β-sheet structures of the preformed Aβ aggregates in living cell. Furthermore, our designed MoS₂-Co is an easy to graft Aβ-target agent that prevents misdirected or undesirable hydrolysis reactions, and has been demonstrated to cross the blood brain barrier. This method can be adapted for hydrolysis of other kinds of amyloids.     

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T_me), mold temperature (T_mo), packing time (t_p), cooling time (t_c), and fill pressure (p_f), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T_me. The rest of the process parameters, T_mo, t_p, t_c, and p_f, were found to be relatively less influential. Warpage increased with elevating T_mo. In contrast, an increase in Pp and T_me caused the warpage to decrease.     

基于空地协同采样的植被覆盖度随机森林估算方法

基于无人机高光谱影像, 建立地形复杂地区植被覆盖度的非参数随机森林回归估算模型。为获得构建随机森林模型所需的足够数量的训练样本, 利用低空无人机搭载的光学相机, 在从地面难以到达的山地、水域和植被茂密区, 通过垂直拍摄获得厘米分辨率的航拍影像, 作为对地面样方采样的补充。首先计算地面数码相机照片和无人机可见光影像的红绿蓝植被指数(red-green-blue vegetation index, RGBVI), 然后使用大津分割法提取样方的植被覆盖信息, 得到构建模型所需的训练样本。在此基础上, 基于2018年8月16—18日在内蒙古自治区察右中旗油娄沟矿区获取的GaiaSky-mini2无人机高光谱影像数据, 利用递归特征消除算法优选参与随机森林回归的特征变量集, 利用空地协同获取的训练样本构建植被覆盖度的随机森林回归估算模型。该模型在测试集上的确定系数R2为0.923, 均方根误差为0.087, 优于常用的像元二分模型, 可用于矿区植被动态信息的精细化监测。     

Multifunctional artificial nacre via biomimetic matrix-directed mineralization

Natural nacre, one of the most studied biological structural materials with delicate hierarchical structures and extraordinary performance, has inspired the design and fabrication of artificial structural ceramics with high fracture toughness. However, to meet the diverse requirements of different applications, future structural materials must be multifunctional with superior mechanical properties, such as strength, hardness, and toughness. Herein, based on the matrix-directed mineralization method for producing biomimetic structural materials, we introduce nanoparticles with different inherent functions into the platelets of artificial nacre via the co-mineralization of aragonite and the nanoparticles. Besides their enhanced mechanical properties, the obtained artificial nacre materials also exhibit different functions depending on the type of the nanoparticles. To extend the versatility of this strategy, the effects of nanoparticles of different sizes and zeta potentials on mineralization are also analyzed. This universal strategy can be applied to the fabrication of other types of functionalized biomimetic structural ceramics that have potential applications in various fields, such as biomedical science.