Production Lot Detection through Synthetic Generated Data

Correctness of information gathered in production environments is an essential part of quality assurance processes in many industries, this task is often performed by human resources who visually take annotations in various steps of the production flow. Depending on the performed task the correlation between where exactly the information is gathered and what it represents is more than often lost in the process. The lack of labeled data places a great boundary on the application of deep neural networks aimed at object detection tasks, moreover supervised training of deep models requires a great amount of data to be available. Reaching an adequate large collection of labeled images through classic techniques of data annotations is an exhausting and costly task to perform, not always suitable for every scenario. A possible solution is to generate synthetic data that replicates the real one and use it to fine-tune a deep neural network trained on one or more source domains to a different target domain. The purpose of this thesis is to show a real case scenario where the provided data were both in great scarcity and missing the required annotations. Sequentially a possible approach is presented where synthetic data has been generated to address those issues while standing as a training base of deep neural networks for object detection, capable of working on images taken in production-like environments. Lastly, it compares performance on different types of synthetic data and convolutional neural networks used as backbones for the model.

Verification of rupture disc sizing of Acrylic Reactor

The primary goal of this thesis is to verify the rupture disc sizing of the acrylic reactor. Primarily the test to check the sizing was divided into several stages. It went on to examine ideas to explain the concern and ethical ways, as well as remedies and suggestions to solve the issues and difficulties that were discovered. This thesis will highlight the gathering and arranging of reaction data (recipe composition, enthalpies, reaction temperature, and catalyst feeding times) of the products to be chosen, in accordance with pre-established criteria. To collaborate with the research and development team in the lab to carry out calorimetric testing for the important recipes that have been identified. The verification of the currently installed Rupture Discs in the plant based on the calorimetric test findings is the final stage. This thesis used two separate calorimetry techniques: Phi-TEC II adiabatic calorimetry and differential scanning calorimetry (DSC). The target of the experiment is to check and confirm the correct size of the reactor rupture disc. Arkema (Boretto/Coatex) plant (Emilia romagna) provided a recipe and a scenario following multiple meetings and discussions. The purpose of this technical paper is to describe the outcomes of adiabatic calorimetry performed at the lab scale so that the computation of the vents for a particular recipe and scenario can be verified.