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To prepare the highly active hydrodesulfurization (HDS) catalyst, modified Zr-KIT-5 materials were synthesized by in situ synthesis method and supported NiMoW trimetallic active phases for dibenzot...

Highly ordered mesoporous silica with Pm3n cubic mesostructure and cube-like morphology was facilely fabricated in a cost-efficient aqueous-phase synthesis system. The obtained cubic mesoporous sil...

Zr modified mesoporous alumina Al-TUD-1 with different Zr content was successfully synthesized via sol–gel method and the optimal thermal treatment time was 4 h. The characterization results of HRTEM, XPS and pyridine FT-IR revealed that Zr atoms acted as electronic promoters not only enhanced the sulfidation degree and modulated the morphology of MoS2 active phases but also brought more acidities into the alumina framework. Additionally, the sulfided catalysts were evaluated using dibenzothiophene (DBT) and FCC diesel as feedstocks, respectively. NiMo/ZrAT-100 catalyst with the largest pore size, the highest active metal sulfidities, as well as the highest total acid and B/L value exhibited the highest HDS (99.1%) and HDN efficiencies (98.7%) in diesel hydrotreating reaction; moreover, it possessed the largest reaction rate constant in DBT HDS reaction. Furthermore, a possible reaction network was also proposed, in which DDS route was the predominant pathway for DBT HDS.

With the increasing development of modern industries, ensuring the safety and reliability of production processes becomes a more and more urgent task. Alarm root cause analysis plays a very significant role in preventing faults of complex industrial processes. Causal network modeling is an important part of alarm root cause analysis. For building a good causal network model, transfer entropy is usually adopted as an effective method. However, there are some problems in determining prediction horizons of transfer entropy. In order to solve these problems and further enhance the performance of the original transfer entropy method, a modified transfer entropy method taking the prediction horizon from one variable to another and to itself simultaneously into consideration is proposed. Moreover, based on data-driven and process knowledge based modeling methods, an approach integrating transfer entropy with superficial process knowledge is designed to correct the false calculation of transfer entropy and then o...

With the development of modern process industries, data-driven fault diagnosis methods have attracted more and more attention. In this paper, a novel nonlinear fault diagnosis method based on multidimensional feature pattern classification (MDFPC) is proposed. The proposed MDFPC method integrates multikernel independent component analysis (MKICA) with adaptive rank-order morphological filter (AROMF). First, some dominant independent components capturing nonlinearity are extracted from the historical process data using the MKICA algorithm, getting the template signal and the testing signal of each fault pattern. Then, a multidimensional signal classification method based on AROMF is developed to achieve the diagnosis of fault patterns. The effectiveness of the proposed fault diagnosis method is demonstrated by carrying out a case study using the Tennessee Eastman process.