Can machine learning improve carbon storage? Synergies of deep learning, uncertainty quantification and intelligent process control

As we transition from fossil fuel to renewable energy, negative emission technologies, such ascarbon capture and storage (CCS), can help us reduce CO2 emissions. Effective CO2 storage requires: (1) detailed site characterization, (2) regular, integrated risk assessment, and (3) flexible design and operation. We believe that recent advances in machine learning coupled with uncertainty quantification and intelligent process control help us with these task and thus im-prove the efficiency and safety of subsurface CO2 storage.

Development of intelligent process control system for image segmentation

The classification and description of the tasks solved using computer vision technologies are given. The use of neural networks to create systems for selecting objects in an image stream is considered in more detail. It also explains what is meant by training a neural network and discusses in detail the main stages of machine learning. The features of the application of convolutional neural networks for the segmentation of image objects, i.e., the selection of objects in the image, are indicated. The choice of the neural network architecture has been made, which has the property of extracting basic information from the image. The characteristics of the segmentation problem and the basic principles of computer vision are given. Conclusions are given on the possible application of the developed neural network model for solving various applied problems.

DEVELOPMENT OF EQUIPMENT MANAGEMENT SYSTEM WITH MONITORING OF WORKING CHARACTERISTICS OF TECHNOLOGICAL PROCESSES

In this work, we have obtained multi-purpose optimization of the baklava production process based on technologies of agent systems and the intelligent process control environment. The general architecture of the control system intelligent environment with agent technologies for recognizing abnormal situations is developed and adaptive baklava production regulators are synthesized. The proposed approach to the construction of an automatic process control system with an intelligent environment is to increase the efficiency of baklava production.

En route to intelligent wood machining – current situation and future perspectives

Wood materials are an important part of our daily life. Besides furniture, doors and window elements, parquet floors, veneering, ply wood, chip- and fibreboards, also structural elements for buildings are typical products. Due to the specific properties, variety and complexity of natural wood, wood materials and wood composites, the machining of parts made out of these materials exhibits specific challenges. In order to further improve productivity, quality and efficiency in wood machining, innovative solutions with respect to tool technology, process planning, machinery, process monitoring and intelligent control are necessary. This keynote paper reviews and summarizes scientific developments in wood machining in recent years. Furthermore, exemplary current an ongoing research activities are introduced. Finally, the paper presents and discusses future potentials regarding new approaches for intelligent process control in wood machining.