1,721,004 research outputs found
Development of Hybrid Model and Optimization of Operating Conditions for a Styrene Monomer Reactor
No full textDevelopment of Hybrid Model and Optimization of Operating Conditions for a Styrene Monomer Reactor
No full textDevelopment of a PC Based Simulator for a Styrene Monomer Reactor Using a Neural Network Hybrid Model
No full text
Preparation of cellular samples using graphene cover and air-plasma treatment for time-of-flight secondary ion mass spectrometry imaging
No full textWe report on sample preparation methods based on plasma treatment for an improvement of multiple molecular ion images of cellular membranes in the ToF-SIMS method. The air-plasma treatment of fixed cellular samples efficiently removed the organic residues of any solutions used during sample preparation and improved the quality of ToF-SIMS images due to the resulting clean surface. We also studied cell preparation methods that combine single-layer graphene covering with air-plasma treatment to achieve a synergistic effect that eliminates background spectra by organic impurities while minimizing morphological cell deformation in a vacuum environmental analysis. When the cellular sample on the glass substrate is completely covered with the single-layer graphene, the cells trapped between the graphene and the substrate can effectively reduce morphological deformation by slow-dehydration. After slow-dehydration of cells is completed inside the graphene-cover, the covered graphene layer can be peeled off by air-plasma treatment, and the unwanted organic residues on the surface of cells and substrate can also be removed by plasma cleaning, thereby much improving ion imaging of cells with the ToF-SIMS method. It is confirmed that the cell samples in which the graphene-cover was removed by air-plasma treatment maintained their morphology well in comparison with the rapid air-dried cells in atomic force microscopy (AFM) and ToF-SIMS images
Proactive Scheduling Strategy Applied to Decoking Operations of an Industrial Naphtha Cracking Furnace System
No full textThe scheduling of decoking operations in a naphtha cracking furnace system is an important issue to ethylene producers, because excessive coke deposits inside the furnace coils can negatively impact plant safety and productivity. For optimal scheduling, accurate online estimation of the thickness of the deposited coke is essential. In practice, the coke thickness can be estimated from various furnace operating Variables, but measurement errors and unexpected changes in the coke growth rate cause significant uncertainties in the estimation. Errors in the coke thickness estimate manifest themselves as gaps between the model prediction and actual measured values of key operating variables such as the pressure drop and the tube temperature. To handle the potential conflicts in an established schedule caused by the uncertainties, we propose to use a "proactive" scheduling strategy. In "reactive" scheduling, rescheduling is performed whenever an unexpected operational problem causes an unscheduled decoking operation, thus making a standing schedule no longer viable. On the other hand, in the "proactive" scheduling strategy, model information, as well as measurement information, are used to determine appropriate rescheduling points before actual operational problems arise. Under the proposed proactive scheduling strategy, the model predictions of the pressure drop and the tube temperature are compared against their measurements while the plant is operating according to a current decoking schedule. Whenever the gap between the model prediction and the measurement is larger than a given threshold value, the model is updated based on the measurements and the scheduling problem is solved again with the updated model information. The new scheduling solution is applied to the operation until the next scheduling point is found. This proactive scheduling procedure is applied to a simulated system of multiple furnaces. The advantages of the proactive scheduling strategy, in terms of productivity and risk management, are shown by comparing it with a reactive scheduling strategy and a heuristic decoking strategy over a large number of scenarios.
- …
