滨海吹填土软弱地基多工法联合处理施工控制措施研究

大面积高精度厂房软基处理对施工方法及控制措施要求较高,因此,研究基于滨海精密厂房吹填土软基处理项目,对两种软弱地基处理方案进行了技术、经济层面的对比,选择了管井降水+强夯+石灰土层多工法联合处理方案,并对多工法施工工艺及施工控制措施进行了总结。通过单桩竖向抗压静荷载试验,试验点的复合地基承载力极限值为130kPa,其符合设计要求,除此还验证了多工法施工方案及控制措施的有效性。

多工法组合下隧道开挖对地层的扰动影响分析

文章运用有限元方法,模拟了台阶法、CD法和CRD法等组合工法应用于隧道开挖的工况,分析了不同工法组合开挖下隧道施工对地层的扰动影响。结果表明:工法纵向组合施工影响程度大于并行组合施工影响程度;工法纵向组合施工时,从小断面过渡向大断面施工比大断面过渡向小断面施工地层沉降位移要稍小;并行组合施工时,地层沉降值近似于两隧洞开挖引起的地层沉降值在对应位置的叠加,以大洞作为隧道开挖先行洞有利于控制施工扰动变形。

平行叠落地铁区间施工地表沉降特征研究

北京某在建地铁区间暗挖段工程北侧为单跨双层拱顶直墙断面,采用PBA法施工;南侧为双洞叠落标准断面,上层采用CRD法施工,下层采用台阶法施工。因此,利用有限元软件进行数值模拟分析,研究了“先小后大”+“先下后上”、“先小后大”+“先上后下”、“先大后小”+“先下后上”以及“先大后小”+“先上后下”4种不同施工顺序下,地铁区间施工对纵向地表沉降与横向地表沉降的影响。研究结果表明:在该工程中,采用“先大后小”+“先下后上”的施工顺序引起的地表沉降值最小。该研究成果可为同类型工程地表变形控制提供参考。

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Alunite processing method selection using the AHP and TOPSIS approaches under fuzzy environment

Alunite is the most important non bauxite resource for alumina. Various methods have been proposed and patented for processing alunite, but none has been performed at industrial scale and no technical,operational and economic data is available to evaluate methods. In addition, selecting the right approach for alunite beneficiation, requires introducing a wide range of criteria and careful analysis of alternatives.In this research, after studying the existing processes, 13 methods were considered and evaluated by 14 technical, economic and environmental analyzing criteria. Due to multiplicity of processing methods and attributes, in this paper, Multi Attribute Decision Making methods were employed to examine the appropriateness of choices. The Delphi Analytical Hierarchy Process(DAHP) was used for weighting selection criteria and Fuzzy TOPSIS approach was used to determine the most profitable candidates. Among 13 studied methods, Spanish, Svoronos and Hazan methods were respectively recognized to be the best choices.

Improved multi-objective artificial bee colony algorithm for optimal power flow problem

The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

Microfluidic technology for multiphase emulsions morphology adjustment and functional materials preparation

Multiphase emulsions could be used as templates in considerable fields such as coating, optical materials, stan- dard particles and biomedicine. Among various emulsion forming methods, microfluidic technology, with good monodispersity, high controllability and operation simplicity, has been widely used in the preparation of multi- phase emulsions with different systems. This review would focus on the basic principles of forming multiphase emulsions, the recent progress in controlling multiphase flow in microfluidics, and preparation of functional ma- terials with microfluidics mainly by the authors＇ research group. We believe that the review will contribute to the readers in this prospective area very well. ~ 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.

Interference of Local Government, Industrial Concentrative Rate and Corporate Diversification

This paper studies the effects of internal and external environments on the strategic decision-making of diversification using the cross-sectional data of China＇s 1 033 non-financial listed companies in Shenzhen and Shanghai Exchange. Corporate external-environment is substituted by index of marketization and market concentrative rate, and internal-environment is substituted by governance framework and financial status. The multi-regression analysis shows that the strategic decision-making of diversification is prominently affected by external factors such as index of marketization and market concentrative rate, and is significantly negative related to the proportion of state-owned shares and corporate shares. There is no correlation between diversification and whether directors hold concurrently, or manager compensation, cash flow, and debt ratio.

Optimization of the Extraction Process of Polysaccharides from Tegillarca granosa by Response Surface Methodology

[ Objective] The aim was to use response surface methodology to determine optimum conditions for extraction of polysaccharides from Tegillarca granosa. [ Method] Response surface methodology with three-factors and throe-levels was carried out for optimizing the extraction process of polysacchafides from Tegillarca granosa. A central composite des（gn including independent variables, such as extraction temperature （A）, extraction time （B）, and ethanol concentration （C） was obtained through Box-Benhnken central combination design. Selected response which evaluates the extraction process was polysacchadde yield. [ Result] The independent variable with the largest effect on response was ethanol concentration （C）. The optimum extraction conditions were found to be extraction temperature 69.6℃, extraction time 6.2 h, and ethanol concen- tration of 78% （V/V）, respectively. Under these conditions, the extraction efficiency of polysaccharide can increase to 1. 635%. [ Coaclusioa] Study on the extraction of polysaccharides from Tegillarca granosa could provide certain theoretical direction for extracting polysaccharides from Tegillarca granosa on a large scale.

Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

A Multi-level Approach for Complex Fault Isolation Based on Structured Residuals

In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.

Collision avoidance planning in multi-robot system based on improved artificial potential field and rules

For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.

Droplet Size Distribution on the Large-holed Compound Sieve Tray in the Spray Regime

The droplet size distribution with large-holed compound sieve tray operating in the spray regime is measured by using a double electrical probes technique in a cold model column of 400 mm diameter. The results indicate that the hole F-factor F0 and surface tension are the main factors which influence the liquid dispersion expressed by the Sauter mean diameter D32. A correlation of D32 on surface tension, viscosity, .F-factor, weir height and liquid flow rate is proposed.

Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.

Study on Extraction Process of Polysaccharides from Chrysanthemum

[Objective] The aim was to study the optimum extraction process of polysaccharide from chrysanthemum.[Method] By dint of orthogonal and single-factor experiments,the influences of microwave efficiency,extraction time,and liquid ratio on the extraction rate of polysaccharides from chrysanthemum were studied.The optimum operation condition by microwave extraction method was determined and was compared with that by traditional extraction method.[Result] The optimum extraction conditions were:800 W,1:15(solid-liquid ratio),and 15 min.The polysaccharides extraction yield under such optimum condition was 5.59%.The extraction yield of impregnation was generally about 2.73%.[Conclusion] Compared with the traditional extraction method,the microwave extraction was simple,convenient,energy saving,and efficient,and was suitable for industrial extraction of polysaccharide.

A Multi-agent Artificial Immune Network Algorithm for the Tray Efficiency Estimation of Distillation Unit

Based on the immune mechanics and multi-agent technology, a multi-agent artificial immune network (Maopt-aiNet) algorithm is introduced. Maopt-aiNet makes use of the agent ability of sensing and acting to overcome premature problem, and combines the global and local search in the searching process. The performance of the proposed method is examined with 6 benchmark problems and compared with other well-known intelligent algorithms. The experiments show that Maopt-aiNet outperforms the other algorithms in these benchmark functions. Furthermore, Maopt-aiNet is applied to determine the Murphree efficiency of distillation column and satisfactory results are obtained.

Multi-factors decision-making entropy method and its application in engineering management

In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.