Excited States and Photodebromination of Selected Polybrominated Diphenyl Ethers: Computational and Quantitative Structure—Property Relationship Studies

This paper presents a density functional theory (DFT)/time-dependent DFT (TD-DFT) study on the lowest lying singlet and triplet excited states of 20 selected polybrominateddiphenyl ether (PBDE) congeners, with the solvation effect included in the calculations using the polarized continuum model (PCM). The results obtained showed that for most of the brominated diphenyl ether (BDE) congeners, the lowest singlet excited state was initiated by the electron transfer from HOMO to LUMO, involving a π–σ* excitation. In triplet excited states, structure of the BDE congeners differed notably from that of the BDE ground states with one of the specific C–Br bonds bending off the aromatic plane. In addition, the partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for a quantitative structure-property relationship (QSPR) study. Based on the previously reported kinetic data for the debromination by ultraviolet (UV) and sunlight, obtained QSPR models exhibited a reasonable evaluation of the photodebromination reactivity even when the BDE congeners had same degree of bromination, albeit different patterns of bromination.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

Trouble Call Analysis in Radial Distribution Feeders Using an Idiotypic Immune System

Utilities typically depend on customers to call and inform them of the service interruptions due to outages by entering their addresses. After sufficient calls are received, the utility is able to pinpoint the location of outages. It is essential for the utility to have a good idea on the location of outages before sending crew for restoration work to optimally utilize their resources. This article uses an idiotypic immune system to model and simulate radial distribution system to identify the operated protective devices due to outages. The incoming calls are modeled as antigens, and the protective devices are modeled as antibodies. Using a differential equation describing dynamics of the immune system, the operated protective devices in the network are identified. A pre-processing technique is presented to handle cases with multiple outages within the same period. Different threshold values for call grouping are tested to determine the most suitable values. Several real-life examples are considered to demonstrate that the proposed method is able to identify the correct protective devices.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

基于竹筋-钢筋骨架的人工假顶首层充填厚度研究及现场应用

采用简支梁理论、ANSYS软件数值模拟与现场试验相结合的方法,对竹筋-钢筋骨架人工假顶进行了可行性、受力行为、破坏方式与合理设计参数研究。结果表明,竹筋-钢筋骨架人工假顶结构可有效保护上部充填体,最优充填厚度为4.75 m; 采用竹筋-钢筋配筋方式可兼顾采场稳定性与经济效益,减少支护成本约15%。     

Identification of digital technologies and digitalisation trends in the mining industry

Digitalisation in mining refers to the use of computerised or digital devices or systems and digitised data that are to reduce costs, improve business productivity, and transform mining practices. However, it remains increasingly difficult for mining companies to decide which digital technologies are most relevant to their needs and individual mines. This paper provides an overview of digital technologies currently relevant to mining companies as presented and discussed by mining journals, the media and insight reports of leading consultancy agencies. Relevant technologies were systematically identified using text-mining techniques, and network analyses established the relations between significant technologies. Results demonstrated that currently 107 different digital technologies are pursued in the mining sector. Also, an analysis of the actual implementation of digital technologies in 158 active surface and underground mines reveals a limited uptake of digital technologies in general and that the uptake increases with the run-of-mine production. Large-scale mining operations appear to select and apply digital technologies suitable to their needs, whereas operations with lower production rates do not implement the currently available digital technologies to the same extent. These minor producers may require other digital transformation solutions tailored to their capabilities and needs and applicable to their scale of operations.     

人工砂混凝土性能试验研究

采用正交试验方法配制了不同人工砂取代率、石粉含量、粉煤灰含量的混凝土, 对比了每组混凝土的坍落度, 研究了3 d、28 d龄期混凝土的抗压性能。结果表明: 最优配比的人工砂取代率、石粉含量和粉煤灰含量分别为10%、3%和5%, 此时28 d抗压强度为40.1 MPa, 满足30 MPa的混凝土设计强度。     

基于改进人工蜂群算法的冗余机器臂逆解研究

针对具有移动关节的7自由度机械臂逆运动学求解困难的问题,提出了一种基于改进人工蜂群算法的逆运动学求解方法。首先根据冗余机械臂的物理结构简化出几何模型,利用D-H法建立运动学模型,由坐标系变换得到正运动学的解。由于该冗余机械臂不满足Pieper准则,难以用传统方法求得封闭解;因此采用改进人工蜂群智能算法,利用位置误差与姿态误差的标准差作为目标函数,求取逆运动学的最优解,并利用Matlab编程进行仿真验证,仿真结果表明该方法准确有效,为具有移动关节的冗余机械臂逆运动学的求解提供了一种新的途径。     

直流电对7B04铝合金固溶与时效组织和性能的影响

本文采用拉伸试验、扫描电子显微镜(SEM)和透射电子显微镜(TEM)等手段,研究了7B04铝合金在施加和不施加直流电条件下,经470℃固溶和100-140℃时效处理后的组织和力学性能。与传统的T6处理(470 oC/30 min+120 oC/24h)相比,固溶和/或时效处理时施加500A的电流,可显著提高GPII区的析出密度,进而提高峰时效状态下的拉伸强度和延伸率。固溶过程中施加直流电可将随后时效合金达到峰值强度的时间明显缩短12h。