scholarly journals Smart Modulation for Control Systems with High Regulation Capabilities for Cooling Systems Optimisation and Carbon Footprint Reduction in Industry

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8578
Author(s):  
Roman Baraniuk ◽  
Welf-Guntram Drossel
Keyword(s):  
Carbon Footprint ◽  
Iterative Process ◽  
Internal Combustion Engines ◽  
Co2 Reduction ◽  
Signal Amplitude ◽  
Machine Learning Techniques ◽  
Drilling Depth ◽  
Cooling Systems ◽  
Precise Shape ◽  
Smart Control

Nowadays, every large enterprise is concerned about reducing CO2 emissions. Along with legislation, management, packaging, and transportation decisions, optimising the operation of automated systems in the industry is important. Overheating processes or large cooling systems of one machine during product assembly may seem minor but at the industry level it is quite significant. Either an optimisation of cooling systems or an intelligent machine control which will prevent heat strokes and allow the transition to passive cooling of the whole system is an important issue for improving machine tools efficiency and contributing therefore to CO2 reduction in the industry sector. This research is a transitional phase from the creation of a control system to solve the problems of resonance in the control of systems with parallel piezo kinematics, which were designed to automate the iterative process of non-circular drilling with a precise shape and the subsequent research on the implementation of smart control to optimise the cooling of industrial machines. The total dynamics of the example system in this research is unknown and consists of the dynamics of electrical converters, piezo kinematics, and mechanics. The control signal of this system is generated by the model of the system state with assumptions and simplifications in combination with machine learning techniques considering the previous errors of the transient characteristics with the possibility of re-drilling without damaging the workpiece and with possibility of further trainings to eliminate the iterative process in general. Algorithms for further training at different resonances with a drilling depth change for cylinders of internal combustion engines are offered. These algorithms are proposed for accurate transmission of the input signal amplitude even in resonant situations, power optimisation, increase the system efficiency, as well as reducing the carbon footprint when used in industry in specific applications.

scholarly journals Simplification of the Model of Piezoelectric Actuator Control Based on Preliminary Measurements

Actuators ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 90
Author(s):  
Roman Baraniuk ◽  
Welf-Guntram Drossel
Keyword(s):  
Experimental Data ◽  
Internal Combustion Engines ◽  
Linear Models ◽  
Black Box ◽  
Combustion Engines ◽  
Model Simplification ◽  
Flow Rates ◽  
Precise Shape ◽  
Actuator Control ◽  
Self Learning

This article describes a mathematical model simplification, designed to automate the iterative process of non-circular drilling with a precise shape. This model has been optimized for systems that already have experimental data for processing and analysis. Additionally, using optimization steps, the model can be used for systems with insufficient experimental data with a self-learning opportunity. The high-end model can be used for drilling systems represented as a “black box” without knowing of any parameters of the system. The simplification and assumptions algorithm is based on controlling the input signal for non-circular drilling in the cylinders of internal combustion engines using a drilling machine controlled by 8 piezoelectric actuators. The total dynamics of this system is unknown and consists of the dynamics of electrical converters, piezo-kinematics, and mechanics. Simplification is carried out starting from the methods of diacoptics for a complex system with different process-flow rates, and ending with one or the sum of linear models valid for a given system of assumptions.

scholarly journals The concept of a new refrigerant in combustion engines in aspect of the requirements of modern drive systems

2019 ◽  
Vol 177 (2) ◽  
pp. 46-49
Author(s):  
Mateusz SZRAMOWIAT
Keyword(s):  
Heat Exchange ◽  
Internal Combustion Engine ◽  
Internal Combustion Engines ◽  
Cooling System ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Cooling Systems ◽  
Drive Systems

The article presents currently applied construction solutions for currently used cooling systems for internal combustion engines. There were presented their defects and possible development directions were indicated. On this basis the concept of a cooling system which will enable the improvement of heat exchange in the internal combustion engine has been proposed.

scholarly journals Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
S. M. Jafari ◽  
H. Mehdigholi ◽  
M. Behzad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Internal Combustion Engines ◽  
Signal Amplitude ◽  
Time Domain Analysis ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Valve Leakage ◽  
Artificial Neural

This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damage in valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and five nodes in the input, hidden, and output layers, respectively. The results of the trained system showed that the AE technique could be used to identify the type of damage and its location.

scholarly journals Application of Combined Developments in Processes and Models to the Determination of Hot Metal Temperature in BOF Steelmaking

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 732
Author(s):  
José Díaz ◽  
Francisco Javier Fernández
Keyword(s):  
Machine Learning ◽  
Carbon Footprint ◽  
Process Model ◽  
Relevant Information ◽  
Metal Temperature ◽  
Machine Learning Techniques ◽  
Hot Metal ◽  
Infrared Thermometry ◽  
Temperature Determination ◽  
Process Computer

Nowadays, the steel industry is seeking to reduce its carbon footprint without affecting productivity or profitability. This challenge needs to be supported by continuous improvements in equipment, methods, sensors and models. The present work exposes how the combined development of processes and models (CDPM) has been applied to the improvement of hot metal temperature determination. The synergies that arise when both sides of this research are simultaneously approached are evidenced. A workflow that takes into account the CDPM approach is proposed. First, a thermal model of the process is developed, making it possible to identify that hot metal temperature is a key lever for carbon footprint reduction. Then, three main alternatives for hot metal temperature determination are compared: infrared thermometry, time-series forecasting and machine learning prediction. Despite considering only few process variables, machine learning techniques succeed in extracting relevant information from process databases. An accuracy close to infrared thermometry is obtained, with a much higher applicability. This research shows that process-model alternatives are complementary when judiciously nested in the process computer routines. Combining measurement and modelling techniques, 100% applicability is achieved with an error reduction of 7 °C.

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