Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

New hybrid CPU-GPU solver for CFD-DEM simulation of fluidized bed

Modeling is an alternative to experiment to explore more in multiphase flows. Various modeling approaches have been developed and used from 1D models in the macro-scale to multidimensional models in the micro scale. Well-known modeling approaches for fluidization systems are TFM and CFD-DEM, both have found many practical applications in fluidization systems. The TFM considers both fluid and particulate phase as interpenetrating continua. In contrast, the CFD-DEM considers the fluid as a continuous fluid in the meso-scale (cell-scale) and the solid as discrete particles in the micro-scale. The translational and rotational motions of individual particles are described by applying Newton’s and Euler’s second laws of motion, respectively. Since the first introduction of CFD-DEM technique by Tsuji et al. (1) and Hoomans et al. (2), different aspects of this modeling approach have been being enhanced and developed. Nowadays, this modeling approach has found many applications in different engineering fields specially fluidization (3). One of the main limitations of this modeling approach is its high computational demand which makes the parallelization necessary in order to model larger systems with more details. A CFD-DEM code comprises of three computational parts: CFD, DEM and coupling. The CFD part can be efficiently parallelized using space decomposition method on the distributed-memory platform like MPI, while the DEM part, due to its low granularity, is better to be parallelized using loop-level parallelization on the shared-memory platform like CUDA. We used a combination of both platforms to speed-up the CFD-DEM code. Figure 1 shows the data transfer between different parts of the code and the platforms that are used for their implementation. As it can be seen, the CFD and coupling parts are parallelized using MPI and executed on multiple CPUs and the DEM part is parallelized using CUDA and executed on a GPU. To solve the Navier-Stokes equations, we used the open-source CFD package, OpenFOAM®, while the code for coupling and DEM calculations were developed internally. The main goal of this programing style was to benefit from the maximum computational power of CPU and GPU in a single PC equipped with a CUDA-capable GPU. This code was successfully utilized for a fluidization system containing 100,000 spherical particles with the mean size of 2200 micrometers and density of 1500 kg/m3. Particles were placed in a cylinder with inner diameter of 0.14 m and height of 1 m. Number of fluid cells in the simulation was 7,400. The superficial gas velocity was 2 m/s. The code was executed on one CPU core of an Intel® core™-i7 processor (3.6 GHz) and an NVIDIA GeForce® GTX 660 Ti GPU. The simulation was continued for 1 second and the execution time was about 1.5 hr. Snapshots of this simulation are shown in Figure 2. These snapshots show the contour of gas velocity field and particles which are colored based on their velocity. This code is in its very first stages of developments and needs optimizations in both coupling and DEM parts to gain more execution speed. Please click Additional Files below to see the full abstract

Comparative Study of Attorney's Intervention in Pre-Trial Stage and Its Influence on Fair Judgment in Iran and the United States of America

In the legal system of Iran and United States of America Attorney possesses the most important position in the Criminal Procedure. This position in Iran and United States of Americaplays a key role in the pre-trial stage. Legal system in the United States of America is called “common law”, and Criminal Procedure in this system is “adversarial”. The court does not exist in this system. Iran's legal system which is almost similar to the French system, has the court body. Despite this difference, in the two criminal justice systems, there are similarities within the pre-trial arrangements (Yousefi, 2011). Therefore, in this study, the influence ofattorney’s intervention in pre-trial stage, on three elements of the most important principles of a fair trial, including the principle of neutrality, equality of arms, not giving judicative role to a pursued person have been reviewed. Finally, it was found that attorney's intervention in the preliminary investigation can have negative effects on the principles of fair trial

On the Presence of Particles at the Wall of Gas Fluidized Beds

In this study, the role of hydrodynamics of the dense bed on the particle convection is outlined. The existing models in the literature suggest a constant decrease of particle-wall contact time with an increase in the gas velocity. However, it has been experimentally shown that the contact time increases both in bubbling and turbulent regimes upon increasing the gas velocity. A comprehensive model is developed to represent such a trend and improve agreement with experimental data presented in literature. Comparison of predicted results with the experimental data from the literature confirms the validity of the present model for the dense bed region of fluidized bed of sand particles

Dynamic Characteristics of Bubbling and Turbulent Fluidization Using Hurst Analysis Technique

A non-intrusive vibration monitoring technique was used to study the flow behavior in a fluidized bed. This technique has several advantages compared to other techniques, such as pressure probes and optical fiber probes which may influence the measurement because they are intrusive. Experiments were conducted in a 15 cm diameter by 2 m tall fluidized bed using 470 micron sand particles. Auto correlation functions, mutual information function and Hurst exponent analyses were used to analyze the fluidized bed hydrodynamics near the transition point from bubbling to turbulent fluidization regime. These methods were able to detect the regime transition point using vibration signals

Energy Analysis on the Effect of Magnetic Field on Nanoparticles Fluidization

Effects of magnetic field strength and direction were studied on the fluidization of titanium oxide nanoparticles (anatase phase) with ferromagnetic iron (III) oxide nanoparticles. The main hydrodynamic structures were defined and studied using wavelet transform. Energy analysis was used to study the effect of the field direction and strength on fluidization. The results suggested that mesostructures (agglomerates) have the most effect on the nanoparticle fluidization characteristics. Higher energy at high field strength for upward direction, suggests more intense interaction between agglomerates in the bed for nanoparticles that result in more stochastic pattern and lower ABF regime characteristics. Downward direction at low magnetic field strength shows improving the fluidization quality by the effect of the vibration of the solenoid. It was observed that at low field strength, vibration has a major effect on fluidization than the magnetic force. At high magnetic field strength, as the magnetic force becomes stronger, the downward field decreases the energy of finer structure (agglomerates) which leads to less movement and resistance against fluidization

Investigating the effects of Lactobacillus casei on some biochemical parameters in diabetic mice

Aims: Diabetes mellitus is a metabolic disorder characterised by inadequate  pancreatic insulin secretion or the insulin present being unable to perform its function properly. Consistent with the beneficial effects of probiotics and their ability to lower glucose levels, an impact on diabetes treatment is also expected. The aim of this study was to evaluate the effect of Lactobacillus casei on various either  biochemical parameters in a diabetic mice model.Methods: In the present study, 24 mice were divided into diabetic and control  groups. Further, each group was categorised into two subgroups. The diabetic and control subgroups were fed carrot juice or Lactobacillus casei in carrot juice. Diabetes was induced by streptozotocin (STZ). For 30 days, the mice were fed 2 ml carrot  juice, and Lactobacillus casei in carrot juice (with lactobacillus 109 cfu/ml) by gavage. Then, blood samples were collected to assay biochemical parameters.Results: The results of this study showed that Lactobacillus casei (ATCC39392) significantly reduced blood glucose (BG) levels in diabetic mice receiving the  probiotic, but did not cause a significant change in BG levels in control mice  receiving the probiotic. When comparing insulin, insulin-like growth factor I (IGF-I) and C-peptide in the four groups, it was found that insulin and C-peptide were  significantly different in all groups except for the control group treated with a mixture of probiotic Lactobacillus casei and carrot juice.Conclusion: Our results showed that probiotic Lactobacillus casei effectively  reduces BG levels in diabetic mice treated with this bacterium.Keywords: diabetes, Lactobacillus casei, probioti