Some Results about the Sample Path Properties of Markov Processes with Independent Self-Similar Components

This paper considers a special class of operator self-similar processes Markov processes {X（t）, t≥0} with independent self-similar components, that is, X （ t ） =（X^1（t）,…,X^d（t））, where {X^i（t）,t≥0}, i=1,2,…,d are d independent real valued self-similar Markov processes. By means of Brel-Cantelli lemma, we give two results about asymptotic property as t→∞ of sample paths for two special classes of Markov processes with independent self-similar components.

Study on Winter Operation Process of the Surface Flow Constructed Wetland in Tianjin Area

[Objective] The research aimed to study winter operation process of the surface flow constructed wetland in ＂rianjin area. [Method] In view of climate characteristics in Tianjin, by the way of running under the ice, winter operation experiment of the surface flow constructed wetland in Tianjin was conducted, with the expectation to get some useful process parameters to run such systems in North China in winter. [ Result] Although purification effect of the sewage by surface flow constructed wetland in winter was worse than that in other seasons （ average reduction of about 20%）, surface flow constructed wetland running under the ice was feasible in Tianjin area. When surface flow constructed wetland in North China ran under ice in winter, it was suggested that the outlet must be located in a low position to prevent to be completely frozen, and running water depth should not be less than 50 -60 cm. The hydraulic load could be raised on the basis of reflux, and hydraulic retention time should maintain less than 4 d to keep water-soil interface not freezing. Inlet water depth should be increased as much as possible to improve temperature in the system. V Conclusion1 The research could provide reference for promotion and application of the surface flow constructed wetland in North China.

Impact of Operational Process Factors on Strategy Implementation in Microfinance Organisations in a Developing Country

This paper sets to explore management perceptions regarding the impact of operational process factors on strategy implementation in microfinance organisations in Kenya.Small and medium enterprises(SMEs)play a critical role in developing countries as a source of employment creation and a basis for industrialization.However,the sector receives inadequate financial support from commercial banks as it is considered to bear high risks and operational costs associate with lending small loan amounts.Microfinance organisations(MFOs)that serve this sector will need to expand their operations to increase their outreach to SMEs.Formulation and implementation of competitive strategies will enable MFOs to achieve growth and sustainability.Content,context and operational process factors appear to have a significance positive relationship to the level of strategy implementation.This paper is focusing on operational process factors impacting on strategy implementation and is part of a full study on the topic of strategy implementation in MFOs.Comprehensive literature review provided the theoretical framework for the study.Primary data were collected by means of a survey obtaining 300 self-administered questionnaires from managers in 135 MFOs in Kenya.The study revealed that the level of strategy implementation in MFOs in Kenya is moderate to high.This study has revealed that the operational process category of factors is more significant to the level of strategy implementation than content and context factors.Five operational process factors critical to strategy implementation include:operational planning and monitoring,management control systems,people-strategy fit,teamwork,and effective communication.Further,the level of strategy implementation has significance positive influence to MFOs’financial sustainability and outreach.Practical guidelines are provided to assist MFOs in developing countries to improve the level of strategy implementation by focusing on operational process factors.

Mechanical Analysis of a Multi-Test String in High-Temperature and High-Pressure Deep Wells

The mechanical behavior of the test string in deep wells is generally relatively complex as a result of the high temperature and high pressure,severe dogleg and buckling effects,which in some circumstances can even lead to string failure.Traditional computational methods for the analysis of these behaviors are often inaccurate.For this reason,here a more accurate mechanical model of the test string is introduced by considering variables such as temperature,pressure,wellbore trajectory,and buckling,as well as combining them with the deformation and string constraint conditions brought in by changes in temperature and pressure during the tripping,setting,and test operations.The model is validated by applying it to a specific high-pressure gas well(located in Northeast Sichuan).

Reinforced temperature control of a reactive double dividing-wall distillation column

The mass and thermal coupling makes the control of the reactive double dividing-wall distillation column(R-DDWDC) an especially challenging issue with a highly interactive nature. With reference to the separation of an ideal endothermic quaternary reversible reaction with the most unfavorable ranking of relative volatilities(A + B ■ C + D with α_(A)>α_(C)>α_(D)>α_(B)), the operation rationality of the R-DDWDC is studied in this contribution. The four-point single temperature control system leads to great steady-state discrepancies in the compositions of products C and D and the reason stems essentially from the failure in keeping strictly the stoichiometric ratio between reactants A and B. A temperature plus temperature cascade control scheme is then employed to reinforce the stoichiometric ratio control and helps to secure a substantial abatement in the steady-state discrepancies. A temperature difference plus temperature cascade control scheme is finally synthesized and leads even to better performance than the most effective double temperature difference control scheme. These outcomes reveal not only the operation feasibility of the R-DDWDC but also the general significance of the proposed temperature difference plus temperature cascade control scheme to the inferential control of any other complicated distillation columns.

Vapor recompressed dividing-wall distillation columns:Structure and performance

Due to the topological structure of double columns and multiple separating sections in dividing-wall distillation columns(DWDCs),the development of vapor recompressed dividing-wall distillation columns(DWDC-VRHPs)represents a challenging issue with great complexities and tediousness.For the separations of light-component dominated and wide boiling-point ternary mixtures,because the purification of the light-component from the intermediate-and heavy-components incurs the primary energy dissipation,the application of vapor recompressed heat pumps(VRHP)should be aimed to reduce the irreversibility and this leads to the generation of the optimum topological structures of the DWDC-VRHPs,i.e.,a DWDC plus a two-stage VRHP.The first-stage VRHP is to preheat feed,not only taking the advantages of the small temperature elevation available but also favoring the mass transfer between the vapor and liquid phases through feed splitting.The second-stage VRHP is to reduce further separation irreversibility.The philosophy can be applied to any DWDCs no matter where the dividing wall locates.Two case studies on the separations of ternary mixtures of benzene,toluene,and o-xylene and n-pentane,n-hexane,and n-heptane demonstrate the economic optimality of the proposed DWDC-VRHPs and reveal the inherent interplay between internal and external process integration.

On a Pair of Operator Series Expansions Implying a Variety of Summation Formulas

With the aid of Mullin-Rota's substitution rule, we show that the Sheffertype differential operators together with the delta operators ? and D could be used to construct a pair of expansion formulas that imply a wide variety of summation formulas in the discrete analysis and combinatorics. A convergence theorem is established for a fruitful source formula that implies more than 20 noted classical fomulas and identities as consequences. Numerous new formulas are also presented as illustrative examples. Finally, it is shown that a kind of lifting process can be used to produce certain chains of(∞~m) degree formulas for m ≥ 3 with m ≡ 1(mod 2) and m ≡ 1(mod 3), respectively.

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Mathematical Apparatus for Selection of Optimal Parameters of Technical, Technological Systems and Materials Based on Vector Optimization

We presented Mathematical apparatus of the choice of optimum parameters of technical, technological systems and materials on the basis of vector optimization. We have considered the formulation and solution of three types of tasks presented below. First, the problem of selecting the optimal parameters of technical systems depending on the functional characteristics of the system. Secondly, the problem of selecting the optimal parameters of the process depending on the technological characteristics of the process. Third, the problem of choosing the optimal structure of the material depending on the functional characteristics of this material. The statement of all problems is made in the form of vector problems of mathematical (nonlinear) programming. The theory and the principle of optimality of the solution of vector tasks it is explained in work of https://rdcu.be/bhZ8i. The implementation of the methodology is shown on a numerical example of the choice of optimum parameters of the technical, technological systems and materials. On the basis of mathematical methods of solution of vector problems we developed the software in the MATLAB system. The numerical example includes: input data (requirement specification) for modeling;transformation of mathematical models with uncertainty to the model under certainty;acceptance of an optimal solution with equivalent criteria (the solution of numerical model);acceptance of an optimal solution with the given priority of criterion.

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.

Continuous synthesis of dolutegravir sodium crystals using liquid-gas heterogeneous microreactor

In this work, a liquid-gas heterogeneous microreactor was developed for investigating continuous crystallization of dolutegravir sodium(DTG), as well as revealing reaction kinetics and mixing mechanism with 3-min data acquisition. The reaction kinetics models were established by visually recording the concentration variation of reactants over time in the microchannel via adding pH-sensitive fluorescent dye. The mixing intensification mechanism of liquid-gas flow was quantified through the fluorescent signal to indicate mixing process, demonstrating an outstanding mixing performance with a mixing time less than 0.1 s. Compared with batch crystallization, continuous synthesis of dolutegravir sodium using liquid-gas heterogenous microreactor optimizes crystal distribution size, and successfully modifies the crystal morphology in needle-like habit instead of rod-like habit. The microreactor continuous crystallization can run for 5 h without crystal blockage and achieve D90 of DTG less than 30 μm. This work provides a feasible approach for continuously synthesizing dolutegravir sodium, and can optimize the existing pharmaceutical crystallization.

Stormwater treatment： examples of computational fluid dynamics modeling

Control of rainfall-runoff particulate matter （PM） and PM-bound chemical loads is challenging; in part due to the wide gradation of PM complex geometries of many unit operations and variable flow rates. Such challenges and the expense associated with resolving such challenges have led to the relatively common examination of a spectrum of unit operations and processes. This study applies the principles of computa- tional fluid dynamics （CFD） to predict the particle and pollutant clarification behavior of these systems subject to dilute multiphase flows, typical of rainfall-runoff, within computationally reasonable limits, to a scientifically acceptable degree of accuracy. The Navier-Stokes （NS） system of nonlinear partial differential equations for multi- phase hydrodynamics and separation of entrained particles are solved numerically over the unit operation control volume with the boundary and initial conditions defined and then solved numerically until the desired convergence criteria are met. Flow rates examined are scaled based on sizing of common unit operations such as hydrodynamic separators （HS）, wet basins, or filters, and are examined from 1 to 100 percent of the system maximum hydraulic operating flow rate. A standard turbulence model is used to resolve flow, and a discrete phase model （DPM） is utilized to examine the particle clarification response. CFD results closely follow physical model results across the entire range of flow rates. Post-processing the CFD predictions provides an in-depth insight into the mechanistic behavior of unit operations by means of three dimensional （3-D） hydraulic profiles and particle trajectories. Results demon- strate the role of scour in the rapid degradation of unit operations that are not maintained. Comparisons are provided between measured and CFD modeled results and a mass balance error is identified. CFD is arguably the most powerful tool available for our profession since continuous simulation modeling.