High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process

CFD-PBM simulation of hydrodynamics of microbubble column with shear-thinning fluid

A numerical simulation was performed to study the hydrodynamics of micro-bubble swarm in bubble column with polyacrylamide (PAM) aqueous solution by using computational fluid dynamics coupled with population balance models (CFD-PBM). By considering rheological characteristics of fluid, this approach was able to accurately predict the features of bubble swarm, and validated by comparing with the experimental results. The gas holdup, turbulent kinetic energy and liquid velocity of bubble column have been elucidated by considering the influences of superficial gas velocity and gas distributor size respectively. The results show that with the rise of the superficial gas velocity, the gas holdup and its peak width increase significantly. Especially, the curve peak corresponding to high gas velocity tends to drift obviously toward the right side. Except for the occurrence of a smooth holdup peak at the column center under the condition of the moderate distributor size, the gas holdups for the small and large distributor sizes become flat in the radial direction respectively. The distribution of turbulent kinetic energy presents an increasingly asymmetrical feature in the radial direction and also its variation amplitude enhances obviously with the rise of gas velocity. The increase in gas distributor size can enhance markedly turbulent kinetic energy as well as its overall influenced width. At the low and moderate superficial gas velocity, the curves of the liquid velocity in radial direction present the Gaussian distributions, whereas the perfect distribution always is broken in the symmetry for high gas velocity. Both liquid velocities around the bubble column center and the ones near both column walls go up consistently with the gas distributor size, especially near the walls at the large distributor size condition.

Plant-wide process monitoring by using weighted copula-correlation based multiblock principal component analysis approach and online-horizon Bayesian method

Multiblock methods have been proposed to capture the complex characteristics of plant-wide monitoring due to the enlargement of process industries. These methods based on automatic sub-block division and copula-correlation, which simultaneously describe the correlation degree and correlation patterns, are designed for sub-block partition. However, the selection of variables for each sub-block through copula-correlation analysis requires a pre-defined cutoff parameter which is difficult to be determined without sufficient prior knowledge, and a "bad" parameter leads to a degraded performance. Therefore, a weighted copula-correlation-based multiblock principal component analysis (WCMBPCA) is proposed. First, the variables in each sub-block are obtained through the copula-correlation analysis-based weighted strategy rather than the cutoff parameter, which highly avoids information loss and prevents "noisy" information. Second, a PCA model is established in each sub-block. Third, a Bayesian inference strategy is used to merge the monitoring results of all sub-blocks. Finally, an online-horizon Bayesian fault diagnosis system is established to identify the fault type of the system based on the statistics of each sub-block. The average detection rate and the average diagnosis rate for numerical example are 77.85% and 98.95%, and that for TE example are 80.63% and 89.50%. Comparing with other candidate methods, the proposed method achieves excellent detection and diagnostic performance.

Research advances on process systems integration and process safety in China

Process systems engineering research focuses on the planning, design, operation, and safety of process systems rather than unit operations. In response to the rapid growth of the chemical process industry in the last 20 years in China, advanced system integration and process safety technologies are investigated and applied for better resource utilization, less environmental impact, and safer working places. In this regard, the review in this article consists of four main achievements: (1) process synthesis, (2) energy system integration, (3) water system integration, and (4) process safety management. The purpose of process synthesis and integration is to improve resource and energy utilization, at the same time lowering by-products and emissions. Optimization is conducted on process structure and operation, following the principles of resource coupling and energy cascade utilization. Typical examples are coupling of coal and hydrogen-rich resources and integration of coal-based polygeneration process of chemicals, electricity, and heat. Energy integration implements the coordinated optimization of total site energy systems. Reviews are made on specific methodologies based on the thermodynamics and applications of design and retrofit in ethylene, oil refining, and synthetic ammonia industries. There are energy savings by 10%–20% and yields increasing by 20%–30%. In addition, waste heat recovery and cold energy utilization are also important research areas. Reviews on the progress of water system integration and its industrial applications are also conducted. It includes the direct reuse, regeneration, and reuse/recycle in water systems and systems with internal water mains. Finally, safety management and technologies are also indispensable technological advancements of the process. The legislation system and the work safety-related standard system have been gradually established and enforced. Process safety research progress is reviewed, and questions are proposed for improving the accident prevention and safety management agenda.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.