Electric-motion in Romania — Overview: Study on machining parameters of EV charging station mechanical components
This paper presents some relevant aspects of charging infrastructure for electric vehicles in Romania. It evidences the charging station and fast charging stations designed and installed by the first Romanian company dedicated 100% to e-mobility, E-Motion Electric. Also, a study on machining (milling) parameters of some mechanical components of electric vehicle (EV) charging station is done and, finally, the regression model for these parameters is determined. This model would be further used for machining process optimization.
Modelling and Controlling of Machining Forces when Milling Polymeric Composites
Polymeric matrix composite materials are used when high characteristics are needed, good resistance under severe working conditions and low specific weight. Implementing polymeric matrix composite materials, specially, glass reinforced ones, resulted in the substituting of classical materials, whose quantities are more, and more, limited. Composite material products can be obtained to their final shape when manufacturing or, when necessary, can be submitted to further machining processes. Polymeric matrix composite materials can be conventionally machined (cutting, turning, drilling, milling or broaching) or / and non-conventionally machined (laser, ultrasound, etc.). Researches on milling machinability of glass fibers reinforced polymeric composites are at their very beginning, in our country, this paper pointing out some important aspects of research on cutting forces in cylindrical-face milling. There is also presented a data measuring, acquisition and processing system for the experimental model, as well as a real time control scheme designed to determine the measuring errors.
Advanced Practical Process Control
1 Introduction to Advanced Process Control Concepts.- 1.1 Process Time Constant.- 1.2 Domain Transformations.- 1.3 Laplace Transformation.- 1.4 Discrete Approximations.- 1.5 z-Transforms.- 1.6 Advanced and Modified z-Transforms.- 1.7 Common Elements in Control.- 1.8 The Smith Predictor.- 1.9 Feed-forward Control.- 1.10 Feed-forward Control in a Smith Predictor.- 1.11 Dahlin's Control Algorithm.- References.- 2 Process Simulation.- 2.1 Simulation using Matlab Simulink.- 2.2 Simulation of Feed-forward Control.- 2.3 Control Simulation of a 2x2 System.- 2.4 Simulation of Dahlin's Control Algorithm.- 3 Process Modeling and Identification.- 3.1 Model Applications.- 3.2 Types of Models.- 3.2.1 White Box and Black Box Models.- 3.2.2 Linear and Non-linear Models.- 3.2.3 Static and Dynamic Models.- 3.2.4 Distributed and Lumped Parameter Models.- 3.2.5 Continuous and Discrete Models.- 3.3 Empirical (linear) Dynamic Models.- 3.4 Model Structure Considerations.- 3.4.1 Parametric Models.- 3.4.2 Non-parametric Models.- 3.5 Model Identification.- 3.5.1 Introduction.- 3.5.2 Identification of Parametric Models.- 3.5.3 Identification of Non-parametric Models.- References.- 4 Identification Examples.- 4.1 SISO Furnace Parametric Model Identification.- 4.2 MISO Parametric Model Identification.- 4.3 MISO Non-parametric Identification of a Non-integrating Process.- 4.4 MIMO Identification of an Integrating and Non-integrating Process.- 4.5 Design of Plant Experiments.- 4.5.1 Nature of Input Sequence.- 4.5.2 PRBS Type Input.- 4.5.3 Step Type Input.- 4.5.4 Type of Experiment.- 4.6 Data File Layout.- 4.7 Conversion of Model Structures.- 4.8 Example and Comparison of Open and Closed Loop Identification.- References.- 5 Linear Multivariable Control.- 5.1 Interaction in Multivariable Systems.- 5.1.1 The Relative Gain Array.- 5.1.2 Properties of the Relative Gain Array.- 5.1.3 Some Examples.- 5.1.4 The Dynamic Relative Gain Array.- 5.2 Dynamic Matrix Control.- 5.2.1 Introduction.- 5.2.2 Basic DMC Formulation.- 5.2.3 One Step DMC.- 5.2.4 Prediction Equation and Unmeasurable Disturbance Estimation.- 5.2.5 Restriction of Excessive Moves.- 5.2.6 Expansion of DMC to Multivariable Problems.- 5.2.7 Equal Concern Errors.- 5.2.8 Constraint Handling.- 5.2.9 Constraint Formulation.- 5.3 Properties of Commercial MPC Packages.- References.- 6 Multivariable Optimal Constraint Control Algorithm.- 6.1 General Overview.- 6.2 Model Formulation for Systems with Dead Time.- 6.3 Model Formulation for Multivariable Processes.- 6.4 Model Formulation for Multivariable Processes with Time Delays.- 6.5 Model Formulation in Case of a Limited Control Horizon.- 6.6 Mocca Control Formulation.- 6.7 Non-linear Transformations.- 6.8 Practical Implementation Guidelines.- 6.9 Case Study.- 6.10 Control of a Fluidized Catalytic Cracker.- 6.11 Examples of Case Studies in MATLAB.- 6.12 Control of Integrating Processes.- 6.13 Lab Exercises.- 6.14 Use of MCPC for Constrained Multivariable Control.- References.- 7 Internal Model Control.- 7.1 Introduction.- 7.2 Factorization of Multiple Delays.- 7.3 Filter Design.- 7.4 Feed-forward IMC.- 7.5 Example of Controller Design.- 7.6 LQ Optimal Inverse Design.- References.- 8 Nonlinear Multivariable Control.- 8.1 Non-linear Model Predictive Control.- 8.2 Non-linear Quadratic DMC.- 8.3 Generic Model Control.- 8.3.1 Basic Algorithm.- 8.3.2 Examples of the GMC Algorithm.- 8.3.3 The Differential Geometry Concept.- 8.4 Problem Description.- 8.4.1 Model Representation.- 8.4.2 Process Constraints.- 8.4.3 Control Objectives.- 8.5 GMC Application to the CSTR System.- 8.5.1 Relative Degree of the CSTR System.- 8.5 2 Cascade Control Algorithm.- 8.6 Discussion of the GMC Algorithm.- 8.7 Simulation of Reactor Control.- 8.8 One Step Reference Trajectory Control.- 8.9 Predictive Horizon Reference Trajectory Control.- References.- 9 Optimization of Process Operation.- 9.1 Introduction to Real-time Optimization.- 9.1.1 Optimization and its Benefits.- 9.1.2 Hierarchy of Optimization.- 9.1.3 Issues to be Addressed in Optimization.- 9.1.4 Degrees of Freedom Selection for Optimization.- 9.1.5 Procedure for Solving Optimization Problems.- 9.1.6 Problems in Optimization.- 9.2 Model Building.- 9.2.1 Phases in Model Development.- 9.2.2 Fitting Functions to Empirical Data.- 9.2.3 The Least Squares Method.- 9.3 The Objective Function.- 9.3.1 Function Extrema.- 9.3.2 Conditions for an Extremum.- 9.4 Unconstrained Functions: one Dimensional Problems.- 9.4.1 Newton's Method.- 9.4.2 Quasi-Newton Method.- 9.4.3 Polynomial Approximation.- 9.5 Unconstrained Multivariable Optimization.- 9.5.1 Introduction.- 9.5.2 Newton's Method.- 9.6 Linear Programming.- 9.6.1 Example.- 9.6.2 Degeneracies.- 9.6.3 The Simplex Method.- 9.6.4 The Revised Simplex Method.- 9.6.5 Sensitivity Analysis.- 9.7 Non-linear Programming.- 9.7.1 The Lagrange Multiplier Method.- 9.7.2 Other Techniques.- 9.7.3 Hints for Increasing the Effectiveness of NLP Solutions.- References.- 10 Optimization Examples.- 10.1 AMPL: a Multi-purpose Optimizer.- 10.1.1 Example of an Optimization Problem.- 10.1.2 AMPL Formulation of the Problem.- 10.1.3 General Structure of an AMPL Model.- 10.1.4 General AMPL Rules.- 10.1.5 Detailed Review of the Transportation Example.- 10.2 Optimization Examples.- 10.2.1 Optimization of a Separation Train.- 10.2.2 A Simple Blending Problem.- 10.2.3 A Simple Alkylation Reactor Optimization.- 10.2.4 Gasoline Blending.- 10.2.5 Optimization of a Thermal Cracker.- 10.2.6 Steam Net Optimization.- 10.2.7 Turbogenerator Optimization.- 10.2.8 Alkylation Plant Optimization.- References.- 11 Integration of Control and Optimization.- 11.1 Introduction.- 11.2 Description of the Desalination Plant.- 11.3 Production Maximization of Desalination Plant.- 11.4 Linear Model Predictive Control of Desalination Plant.- 11.5 Reactor problem definition.- 11.6 Multivariable Non-linear Control of the Reactor.- References.- Appendix I. MCPC software guide.- I.1 Installation.- I.2 Model identification.- I.2.1 General process information.- I.2.2 Identification data.- I.2.3 Output details.- I.3 Controller design.- I.4 Control simulation.- I.5 Dealing with constraints.- I.6 Saving a project.- Appendix II. Comparison of control strategies for a hollow shaft reactor.- II.1 Introduction.- II.2 Model Equations.- II.3 Proportional Integral Control.- II.4 Linear Multivariable Control.- II.5 Non-linear Multivariable Control.- References.