A Blended Empirical Shot Stream Velocity Model for Improvement of Shot Peening Production

Peening intensity and coverage are vital measurement outputs to quantify the quality of a peening process in surface enhancement operation of metal parts. In practice, these parameters can only be measured offline upon process completion, which are not suitable for online tracking and operation. Instead, shot stream velocity can be used as a real-time monitoring parameter to bridge operational inputs to the outputs. As such, a robust and accurate shot stream velocity model is needed for real-time tracking. In this study, we propose a blended practical model for shot stream velocity to address the issues. The model is constructed using regression algorithm based on the blended candidate functions, which are developed from experimental data and nature of the particle-air flow inside the system. Obtained model is validated against experimental data for different conditions. Calculated velocities are in good agreement with the measurements. In addition, applications of the model in predicting the shot stream velocity for different peen types, different peening intensities and coverages. The obtained results are comparable to the measurement data. Furthermore, a single-input and single-output model-based control is developed from proposed shot stream velocity model. The developed control system is robust, accurate and reliable. It implies that the developed model can be used to provide necessary information as well as in optimal process control development to improve and accelerate the peening processes to reduce cost and time of actual productions.

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high.

Optimal Control of the Separation Process with Flow Restrictions

Here we formulate the problem of optimal process control with distributed parameters, taking into account the constraints on the control and associated flows. The necessary optimality conditions are obtained. The analysis of the stationarity conditions is carried out and the method for constructing the domain of admissible controls is proposed. The developed optimization method is applied in the automation of industrial distillation plants for the sulfuric acid alkylation of isobutane with butylenes, ortho-xylene production, etc.

Dynamic model of nuclear power plant steam turbine

The paper presents the dynamic multivariable model of Nuclear Power Plant steam turbine. Nature of the processes occurring in a steam turbine causes a task of modeling it very difficult, especially when this model is intended to be used for on-line optimal process control (model based) over wide range of operating conditions caused by changing power demand. Particular property of developed model is that it enables calculations evaluated directly from the input to the output, including pressure drop at the stages. As the input, model takes opening degree of valve and steam properties: mass flow and pressure. Moreover, it allows access to many internal variables (besides input and output) describing processes within the turbine. The model is compared with the static steam turbine model and then verified by using archive data gained from researches within previous Polish Nuclear Power Programme. Presented case study concerns the WWER-440 steam turbine that was supposed to be used in Żarnowiec. Simulation carried out shows compliance of the static and dynamic models with the benchmark data, in a steady state conditions. Dynamic model also shows good behavior over the transient conditions.