Synergism between lignite and high-sulfur petroleum coke in CO2 gasification

High-sulfur petroleum coke (PC) as solid waste has high treatment cost. Gasification technology can utilize PC and lignite for co-gasification. Organically combining the two is the key to expanding the adaptability of gasification raw materials. This work used thermal analysis technology to study the gasification reaction of PC and lignite systems in a CO2 atmosphere. The results show that the starting and end temperatures of the co-gasification of lignite/high-sulfur PC are lower than those of pure coke. The improved carbonization rate and gasification reaction index indicate that lignite improves the gasification performance. The gasification synergy factors are all greater than 1, indicating that the co-gasification process produces obvious synergism, and the synergism is more obvious in the gasification stage after 800°C. The lignite ash is gradually enriched on the surface of high-sulfur PC with the temperature increase, and the Ca and Fe elements have an obvious catalytic effect, but the catalytic effect has a saturation value. Ashes from lignite used as a multi-component gasification catalyst can increase the overall reactivity in the lignite/high-sulfur PC system, which can broaden the selection of gasification raw materials, and make efficient use of the resource characteristics of both

A Trajectory Tracking Approach for Aerial Manipulators Using Nonsingular Global Fast Terminal Sliding Mode and an RBF Neural Network

An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller

Robust Control Based on Adaptive Neural Network for the Process of Steady Formation of Continuous Contact Force in Unmanned Aerial Manipulator

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness

A Trajectory Tracking Approach for Aerial Manipulators Using Nonsingular Global Fast Terminal Sliding Mode and an RBF Neural Network

An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller

Preparing coal water slurry from BDO tar to achieve resource utilization: gasification process of BDO tar-coal water slurry

1, 4-Butanediol (BDO) is an important organic and fine chemical raw material, but the waste liquid (BDO tar) discoal charged from the BDO production plant is complex in composition, contains salt, and is complicated to handle. In this study, BDO tar was treated by the method of waste-coal water slurry, and the gasification process of blending BDO tar was studied. The results show that as the BDO tar content increases, the organic component in the BDO tar causes the temperature point corresponding to the peak of the maximum reaction rate to migrate to the high temperature zone during the initial temperature to 150 °C. In the temperature range of 200 °C~300 °C, the weight loss of BDO tar leads to a significant weight loss peak of TG curves. From 600 °C to the final reaction temperature range, the alkali metal Na enriches the surface of the coal char with more active “spot”, and due to the alkali metal Na limits the graphitization of coal char, the active sites increase, which increases the coal char gasification reaction activity

Multiscale analysis of fine slag from pulverized coal gasification in entrained-flow bed

Abstract Fine slag (FS) is an unavoidable by-product of coal gasification. FS, which is a simple heap of solid waste left in the open air, easily causes environmental pollution and has a low resource utilization rate, thereby restricting the development of energy-saving coal gasification technologies. The multiscale analysis of FS performed in this study indicates typical grain size distribution, composition, crystalline structure, and chemical bonding characteristics. The FS primarily contained inorganic and carbon components (dry bases) and exhibited a "three-peak distribution" of the grain size and regular spheroidal as well as irregular shapes. The irregular particles were mainly adsorbed onto the structure and had a dense distribution and multiple pores and folds. The carbon constituents were primarily amorphous in structure, with a certain degree of order and active sites. C 1s XPS spectrum indicated the presence of C–C and C–H bonds and numerous aromatic structures. The inorganic components, constituting 90% of the total sample, were primarily silicon, aluminum, iron, and calcium. The inorganic components contained Si–O-Si, Si–O–Al, Si–O, SO4 2−, and Fe–O bonds. Fe 2p XPS spectrum could be deconvoluted into Fe 2p 1/2 and Fe 2p 3/2 peaks and satellite peaks, while Fe existed mainly in the form of Fe(III). The findings of this study will be beneficial in resource utilization and formation mechanism of fine slag in future

The characteristics and clinical relevance of tumor fusion burden in head and neck squamous cell carcinoma

Abstract Background Recent studies suggest that tumor fusion burden (TFB) is a hallmark of immune infiltration in prostate cancer, the correlation of TFB with immune microenvironment, and genomic patterns in head and neck squamous cell carcinomas (HNSC) remain largely unclear. Methods Gene fusion, genomic, transcriptomic, and clinical data of HNSC patients from the cancer genome atlas (TCGA) database were collected to analyze the correlation of TFB with mutation patterns, tumor immune microenvironment, and survival time in HNSC patients. Results Human papillomavirus (HPV) (−) patients with low TFB exhibited significantly enhanced CD8+ T cells infiltration and cytolysis activity and increased level of interferon‐gamma (IL‐γ), human leukocyte antigen (HLA) class I, and chemokines. Moreover, TFB was positively correlated with TP53 mutation, score of gene copy number, and loss of heterozygosity (LOH), as well as the biological progress of epithelial‐mesenchymal transition (EMT), metastasis, and stem cell characteristics. Further analysis revealed that HPV (−) HNSC patients with low TFB have a better prognosis. Conclusions Our data revealed the correlation of TFB with tumor immune microenvironment and predictive features for immunotherapy, implying tumors with low TFB may be potential candidates for immunotherapeutic agents. Moreover, the TFB low group had prolonged overall survival (OS) in the HPV (−) HNSC cohort