The goal of the proposed computational model was to evaluate the dynamical properties of air gauges in order to exploit them in such industrial applications as in-process control, form deviation measurement, dynamical measurement. The model is based on Reynolds equations complemented by the k-ε turbulence model. The boundary conditions were set in different areas (axis of the chamber, side surfaces, inlet pipeline and outlet cross-section) as Dirichlet's and Neumann's ones. The TDMA method was applied and the efficiency of the calculations was increased due to the "line-by-line" procedure. The proposed model proved to be accurate and useful for non-stationary two-dimensional flow through the air gauge measuring chamber.

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.

The paper presents the method of on-line diagnostics of the bed temperature controller for the fluidized bed boiler. Proposed solution is based on the methods of statistical process control. Detected decrease of the bed temperature control quality is used to activate the controller self-tuning procedure. The algorithm that provides optimal tuning of the bed temperature controller is also proposed. The results of experimental verification of the presented method is attached. Experimental studies were carried out using the 2 MW bubbling fluidized bed boiler.

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.

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.

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.

The effects of different types of process control agents (PCA) on the microstructure evolution of Ni-based oxide dispersion-strengthened superalloy have been investigated. Alloy synthesis was performed on elemental powders having a nominal composition of Ni-15Cr-4.5Al-4W-2.5Ti-2Mo-2Ta-0.15Zr-1.1Y2O3 in wt % using high energy ball milling for 5 h. The prepared powders are consolidated by spark plasma sintering at 1000oC. Results indicated that the powder ball-milled with ethanol as PCA showed large particle size, low carbon content and homogeneous distribution of elemental powders compared with the powder by stearic acid. The sintered alloy prepared by ethanol as PCA exhibited a homogeneous microstructure with fine precipitates at the grain boundaries. The microstructural characteristics have been discussed on the basis of function of the PCA.

The paper provides statistical analysis of the photographs of four various granular materials (peas, pellets, triticale, wood chips). For analysis, the (parametric) ANOVA and the (nonparametric) Kruskal-Wallis tests were applied. Additionally, the (parametric) two-sample t-test and (non-parametric) Wilcoxon Rank-Sum Test for pairwise comparisons were performed. In each case, the Bonferroni correction was used. The analysis shows a statistical evidence of the presence of differences between the respective average discrete pixel intensity distributions (PID), induced by the histograms in each group of photos, which cannot be explained only by the existing differences among single granules of different materials. The proposed approach may contribute to the development of a fast inspection method for comparison and discrimination of granular materials differing from the reference material, in the production process.

This study presents cause-effect dependencies between inputs and outputs of business transitions that are software objects designed for processing information-decision state variables in integrated enterprise process control (EntPC) systems. Business transitions are elementary components of controlling units in enterprise processes that have been defined as self-controlling, generalized business processes, which may serve not only as business processes but also as business systems or their roles. Business events, which have zero durations by definition, are interpreted as executions of business actions that are main operations of business transitions. Any ordered set of business actions, performed in the controlling unit of a given enterprise process and attributed to the same discrete-time instant, is referred to as ‘the information-decision process’. The i-d processes may be substituted by managerial business processes, performed on the lower organizational level, where durations of activity executions are greater than zero, but discrete-time periods are considerably shorter. In such a case, procedures of business actions are performed by corresponding activities of managerial processes, but on the level of business transitions the durations of their executions are imperceptible, and many different business events may occur at the same discrete-time instant. It has been demonstrated in the paper how to control business actions to ensure that a given i-d state variable may not change more than once at a given instant. Furthermore, the rules of designing the i-d process structures, which prevent random changes of transitory states, have been presented.

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.

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.