A Self-Learning Process Modeling Method to Optimize Upstream Operations

Detecting motor bearing faults by stator currents is of great importance as it improves the adaptability of measurement means to different environments and reduces the number of sensors. Therefore, many studies have been conducted to investigate bearing faults by constructing motor models, most of which have used signal models to simulate the dynamics of the bearings. However, the signal model may be exposed to the issue that the nonlinearities in the bearing operation are neglected, thus oversimplifying the coupling effects between the electromagnetic and dynamics models. Hence, a coupled electromagnetic–dynamic modeling method for induction motors based on multiple coupled circuit theory and the rotor-bearing dynamics model is proposed in this study to implement the coupled simulation of electromagnetic and dynamic models. The air gap length and rotor velocity are used as coupled parameters for the calculation of stator–rotor mutual inductance and ball contact deformation, respectively. The simulation results show that the proposed model can effectively implement the electromagnetic–dynamic coupled and reflect the bearing fault characteristics in the current signal. Experiments were conducted on induction motors with typical winding configurations under laboratory conditions. The comparison results verify the effectiveness of the proposed modeling method.

The fiber-reinforced polymer composites are important alternative for conventional structural materials because of their excellent comprehensive performance and weight reduction. The mechanical properties of such composite materials are mainly determined by the fiber orientation induced through practical manufacturing process. In the study, a through process modeling (TPM) method coupling the microstructure evolution and the mechanical properties of fiber-reinforced composites in practical processing is presented. The numerical methodology based on the finite volume method is performed to investigate three-dimensional forming process in the injection molding of fiber-reinforced composites. The evolution of fiber orientation distribution is successfully predicted by using a reduced strain closure model. The corresponding finite volume model for TPM is detailedly derived and the pressure implicit with splitting of operators (PISO) algorithm is employed to improve computational stability. The flow-induced multilayer structure is successfully predicted according to essential flow characteristics and the fiber orientation distribution. The mechanical properties of such anisotropy composites is further calculated based on the stiffness analysis and the Tandon–Weng model. The improvement of mechanical properties in each direction of the injection molded product are evaluated by using the established mathematical model and numerical algorithm. The influences of the geometric structure of injection mold cavity, the fiber volume fractions, and the fiber aspect ratios on the mechanical properties of composite products are further discussed. The mathematical model and numerical method proposed in the study can be successfully adopted to investigate the structural response of composites in practical manufacturing process that will be helpful for optimum processing design.

This paper reports a preliminary study for use of a fully dynamic vehicle process model in combined underwater communication and navigation (cooperative navigation) of underwater vehicles equipped with an acoustic modem, attitude, and depth sensors, but lacking a Doppler velocity log (DVL), and a surface vehicle equipped with an acoustic modem and GPS. We report both simulation and at-sea experimental trials with the JHU Iver3 autonomous underwater vehicle (AUV). The case of underwater vehicle navigation without a DVL is of interest in several use-cases including (a) small and low-cost underwater vehicles for which DVLs may be impractical or infeasible due to their size and cost and (b) for missions in which the vehicle's altitude above the sea floor (or depth beneath overhead ice) exceeds the DVL acoustic bottom-lock range. To the best of our knowledge, all previous studies on cooperative navigation have reported use of a kinematic process model, which works well in the presence of frequent, high-accuracy velocity measurements, as is the case when the vehicle is equipped with a DVL. This preliminary study suggests that the dynamical process model may offer a significant advantage over the purely kinematic model in the absence of frequent, high-accuracy velocity measurements, as is the case when the submerged vehicle is not equipped with a DVL.

Dynamic simulation is a powerful tool for chemical process optimizing design, which gives an intuitionistic sight for engineer to understand chemical process and analyzing process design. In this work, a dynamic simulation model for the full flow isopropyl benzene plant is presented. The dynamic model is based on detailed configuration of equipment data from a 300,000 t/a isopropyl benzene plant. It was discovered that the benzene column has the problem of pressure fluctuation. The cause of this fluctuation was analysed and the solutions to solve problem were presented by using dynamic model. Emergency condition, and partial power failure, both were considered for isopropyl benzene column design. The dynamic relieving rate was calculated, and the results compared with that calculated from conventional method. In order to avoiding the relieving rate, high integrated protective system (HIPPS) was applied to the dynamic model. The results shown that (1) The reason of benzene column pressure fluctuation is that the solubility of propane in condensate is sensitive to process parameters. A pressure control scheme with tuned PID parameters was proved in good performance; (2) The relieving rate of isopropyl benzene column calculated from dynamic model decrease 65% compared with that of conventional method; (3) The relieving rate of isopropyl benzene column was cut to zero by using HIPPS.

Clinic appointment cancellation and ‘no-shows’ by patients can disturb day-to-day scheduling and lead to inefficient resource allocation and lost revenue. This paper presents an approach for dynamic process modelling and analysis of patients’ no-show rates in conjunction with the overbooking strategy. To this end, a dynamic discrete event simulation model of a clinic operation is developed to assess current clinic performance under various no-show rates and patterns and test several overbooking strategies to improve clinic’s efficiency and enhance performance. The approach significance includes the analysis of variability in scheduling and treatment times, the impact of patients reneging, and walk-in patients. In addition, the paper develops a unified clinic utility function that analyses the impact of overbooking on clinic revenue (patients treated), capacity (utilisation), and customer service (wait time). An example of a general practice (GP) healthcare clinic is used to illustrate the development and application of the proposed approach.

The swath-plate engine is an engine with high pressure air as working medium, and it is designed to meet the underwater launch. In order to explore the performance of the engine, the dynamic model is built by using the method of Kane on the basis of the kinematics and dynamic analysis of the main parts of the engine such as the pistons and links, etc. The simulation of the work process of the engine is realized by the use of the simulation program based on the dynamic model. The prototype engine is tested on the experiment system. The comparison of the experimental results and simulation results show that the dynamic simulation model is basically correct and can be used to optimize the engine design.

Aiming at the issue of inaccurate design of multistage spiral pair structures due to the inaccuracy of the traditional transmission efficiency calculation formula for double-screw swinging hydraulic cylinders, which are used in steering gear, a new transmission efficiency calculation formula for these cylinders is proposed through mathematical modeling and dynamic analysis. Corresponding flexible dynamic and rigid dynamic simulation models of the double-screw swinging hydraulic cylinder are established to investigate the output characteristics, load startup characteristics, and maximum torque of the spiral pair. The accuracy of the flexible dynamic model and the rigid dynamic model is compared, and an experimental validation is conducted using a hydraulic test bench. The following conclusions are drawn: the calculation accuracy of the new transmission efficiency formula is improved by 21% compared to the traditional formula; under rated load pressure, the output torque accuracy of the flexible dynamic simulation model is improved by 55%. The flexible dynamic simulation model's closer alignment with experimental results proves its necessity. The research offers new simulation tools and a theoretical basis for the development of high-precision efficiency prediction models, holistic design, and optimization of double-helical oscillating hydraulic cylinders for underwater equipment.

Many governments who preside over liberalised energy markets are developing policies aimed at promoting investment in renewable generation whilst maintaining the level of security of supply customers have come to expect. Of particular interest is the mix and amount of generation investment over time in response to policies promoting high penetrations of variable output renewable power such as wind. Modelling the dynamics of merchant generation investment in market environments can inform the debate. Such models need improved methods to calculate expected output, costs and revenue of thermal generation subject to varying load and random independent thermal outages in a power system with high penetrations of wind. This paper presents a dynamic simulation model of the aggregated Great Britain (GB) generation investment market. The short-term energy market is simulated using probabilistic production costing based on the Mix of Normals distribution technique with a residual load calculation (load net of wind output). Price mark-ups due to market power are accounted for. These models are embedded in a dynamic model in which generation companies use a Value at Risk (VaR) criterion for investment decisions. An `energy-only' market setting is used to estimate the economic profitability of investments and forecast the evolution of security of supply. Simulated results for the GB market case study show a pattern of increased relative security of supply risk during the 2020s. In addition, fixed cost recovery for many new investments can only occur during years in which more frequent supply shortages push energy prices higher. A sensitivity analyses on a number of key model assumptions provides insight into factors affecting the simulated timing and level of generation investment. This is achieved by considering the relative change in simulated levels of security of supply risk metric such as de-rated capacity margins and expected energy unserved. The model can be used as a decision support tool in policy design, in particular how to address the increased `energy-only market revenue risk facing thermal generation, particularly peaking units, that rely on a small number of high price periods to recover fixed costs and make adequate returns on investment.

This paper proposes a new kernel method for dynamic process modelling. The developed algorithm is titled Adaptive Reduced Kernel Partial Least Squares (ARKPLS). The developed ARKPLS uses the Reduced KPLS technique in an offline scenario in order to build a model which have a small parameter number after that, the number of the retained parameters are update in an online scenario. The suggested technique has been used to identify a nonlinear process.

Dynamic Process Model Development and Validation with Transient Plant Data Collected from an MEA Test Campaign at the CO2 Technology Center Mongstad

Dynamic Modelling and Simulation of Fast Pyrolysis Process Integrated with Circulating Fluidized Bed Boiler

We propose and investigate dynamic spatial-temporal point process models for independent and interacting events. The models for independent events are dynamic spatial-temporal Poisson point process (DSTPPP) model that account for temporal and spatial clustering. The models proposed for events with interaction are Markov (Gibbs) space-time point process models. We model the intensity function of a DSTPPP via conditioning arguments that allow for additional interpretations and inclusion of well-known point process models as special cases. Depending on the nature of the questions to be answered, the in- tensity function of a DSTPPP can be modeled in several ways. First, we develop models via conditioning on the time component of the events and then consider conditioning on the location component of the events. Modeling, simulation and computation are accomplished in a fully hierarchical Bayesian framework. Finally, we focus on dynamic marked Markov space-time point processes, where the events are allowed to interact with each other across time and space. Once again the hierarchical Bayesian framework is invaluable in this case, since it allows us to introduce dynamic process models via conditioning. The methodolo- gies are illustrated using simulated data, and several applications, including modeling and inference for earthquake events in California, and tornado events in Missouri.

This paper presents a dynamic simulation model for visual servoing of a robot manipulator using image based strategy. The modeling takes into account the dynamics of the camera velocity inner loop as well as a model of the visual sensor that consists of the camera and feature extraction block. Considering the inner loop and the visual sensor models, the dynamic simulation model of the visual servo open loop was developed. Using this open loop model, an image based controller was derived for the visual servoing system. Finally, the developed dynamic simulation model has been tested, including open-loop validation, closed-loop validation and comparisons between simulation results and real images obtained with a 6 DOF ABB robot having an eye-in-hand camera configuration.

Simulation and sensitivity analysis of spiral wound reverse osmosis process for the removal of dimethylphenol from wastewater using 2-D dynamic model

A LASSO-based batch process modeling and end-product quality prediction method