Control of the technological processes of coal enrichment takes place in the presence of wide disturbances. Thus, one of the basic tasks of the coal enrichment process control systems is the stabilization of coal quality parameters at a preset level. An important problem is the choice of the controller which is robust for a variety of disturbances. The tuning of the controller parameters is no less important in the control process . Many methods of tuning the controller use the dynamic characteristics of the controlled process (dynamic model of the controlled object). Based on many studies it was found that the dynamics of many processes of coal enrichment can be represented by a dynamic model with properties of the inertial element with a time delay. The identification of object parameters (including the time constant) in industrial conditions is usually performed during normal operation (with the influence of disturbances) from this reason, determined parameters of the dynamic model may differ from the parameters of the actual process. The control system with controller parameters tuned on the basis of such a model may not satisfy the assumed control quality requirements. In the paper, the analysis of the influence of changes in object model parameters in the course of the controlled value has been carried out. Research on the controller settings calculated according to parameters T and τ were carried out on objects with other parameter values. In the studies, a sensitivity analysis method was used. The sensitivity analysis for the three methods of tuning the PI controller for the coal enrichment processes control systems characterized by dynamic properties of the inertial element with time delay has been presented. Considerations are performed at various parameters of the object on the basis of the response of the control system for a constant value of set point. The assessment of considered tuning methods based on selected indices of control quality have been implemented.
In the article, the authors analyze and discuss several models used to the calculation of air gauge characteristics. The model based on the actual mass flow (which is smaller than the theoretical one) was proposed, too. Calculations have been performed with a dedicated software with the second critical parameters included. The air gauge static characteristics calculated with 6 different models were compared with the experimental data. It appeared that the second critical parameters model (SCP) provided the characteristics close to the experimental ones, with an error of ca. 3% within the air gauge measuring range.
In this work problems associated with requirements related to pollution emissions in compliance with more restrictive standards, low-emission combustion technology, technical realization of the monitoring system as well as algorithms allowing combustion process diagnostics are discussed. Results of semi-industrial laboratory facility and industrial (power station) research are presented as well as the possibility of application of information obtained from the optical fibre monitoring system for combustion process control. Moreover, directions of further research aimed to limit combustion process environmental negative effects are presented.
The paper presents an analysis of SPC (Statistical Process Control) procedures usability in foundry engineering. The authors pay particular attention to the processes complexity and necessity of correct preparation of data acquisition procedures. Integration of SPC systems with existing IT solutions in area of aiding and assistance during the manufacturing process is important. For each particular foundry, methodology of selective SPC application needs to prepare for supervision and control of stability of manufacturing conditions, regarding specificity of data in particular “branches” of foundry production (Sands, Pouring, Metallurgy, Quality).
Statistical Process Control (SPC) based on the well known Shewhart control charts, is widely used in contemporary manufacturing industry, including many foundries. However, the classic SPC methods require that the measured quantities, e.g. process or product parameters, are not auto-correlated, i.e. their current values do not depend on the preceding ones. For the processes which do not obey this assumption the Special Cause Control (SCC) charts were proposed, utilizing the residual data obtained from the time-series analysis. In the present paper the results of application of SCC charts to a green sand processing system are presented. The tests, made on real industrial data collected in a big iron foundry, were aimed at the comparison of occurrences of out-of-control signals detected in the original data with those appeared in the residual data. It was found that application of the SCC charts reduces numbers of the signals in almost all cases It is concluded that it can be helpful in avoiding false signals, i.e. resulting from predictable factors.
The proportional-integral-derivative (PID) controllers have experienced series of structural modifications and improvements. Example of such modifications are set-point weighting and fractional ordering. While the former is to achieve two-degree-of-freedom (2DOF) ability of set-point tracking and disturbance rejection, the latter is to ensure smooth control action. Therefore, this paper reviews various forms of PID controllers and provides a comparative analysis of 2DOF PID and 2DOF fractional order PID (FOPID) controllers. The paper also discusses the conversion of one PID form to another. For the comparative analysis of the various controllers, a class of unstable systems are considered. Simulation result shows that in most cases the conversion from one form to another does not significantly affect the performance of the system. It is also observed that the 2DOF controllers (2DOF PID and 2DOF FOPID) improved significantly the performance of the ordinary PID controllers.
Achieving control of coating thickness in foundry moulds is needed in order to guarantee uniform properties of the mould but also to achieve control of drying time. Since drying time of water based coatings is heavily dependent on the amount of water present in the coating layer, a stable coating process is prerequisite for a stable drying process. In this study, we analyse the effect of different variables on the coating layer properties. We start by considering four critical variables identified in a previous study such as sand compaction, coating density, dipping time and gravity and then we add centre points to the original experimental plans to identify possible non-linear effects and variation in process stability. Finally, we investigate the relation between coating penetration (a variable that is relatively simple to measure in production) and other coating layer thickness properties (relevant for the drying process design). Correlations are found and equations are provided. In particular it is found that water thickness can be directly correlated to penetration with a simple linear equation and without the need to account for other variables.
There exist numerous modelling techniques and representation methods for digital control algorithms, aimed to achieve required system or process parameters, e.g. precision of process modelling, control quality, fulfilling the time constrains, optimisation of consumption of system resources, or achieving a trade-off between number of parameters. This work illustrates usage of Finite State Machines (FSM) modelling technique to solve a control problem with parameterized external variables. The structure of this work comprises six elements. The FSM is presented in brief and discrete control algorithm modelling is discussed. The modelled object and control problem is described and variables are identified. The FSM model is presented and control algorithm is described. The parameterization problem is identified and addressed, and the implementation in PLC programming LAD language is presented. Finally, the conclusion is given and future work areas are identified.
The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.