Experimental evaluation of MPC-based anti-surge and process control for electric driven centrifugal gas compressors

The present work concerns model predictive control (MPC) of centrifugal gas compressors and describes the development of an MPC application for the tasks of anti-surge and process control. More specifically, the MPC formulation focuses on the question of how the transient manipulation of driver torque can be used to improve the performance of anti-surge and process control. For the purpose of testing and validating the proposed control algorithm, an experimental compressor test rig is presented, which is designed to mimic a typical centrifugal compressor application in the oil and gas industry. Modeling and parameter identification of the experimental setup is followed by the realization of the MPC solution on an embedded system to comply with the stringent real-time requirements for anti-surge control. Testing is performed with experiments using suction and discharge side disturbances, which are created by rapid valve closures. For comparison the same tests are repeated with conventional control approaches. The test results indicate improvements in maintaining the distance to surge by up to 11%, while at the same time reducing the process control settling time by up to 50%.     

Dynamic behavior of droplet transport on realistic gas diffusion layer with inertial effect via a unified lattice Boltzmann method

The dynamic behavior of liquid droplets on a reconstructed real gas diffusion layer (GDL) surface with the inertial effect produced by the three dimensional (3D) flow channel is investigated using an improved pseudopotential multiphase model within the unified lattice Boltzmann model (ULBM) framework, which can realize thermodynamic consistency and tunable surface tension. The microstructure of the GDL (Toray-090) including carbon fibers and polytetrafluoroethylene (PTFE) is reconstructed by a stochastic and mixed-wettability model. The critical force formulation for the Cassie-Wenzel transition of a droplet on GDL surface is derived. The effects of inertia and contact angles on the liquid droplet transport process on a reconstructed real GDL surface with a 3D flow channel are investigated. The results show the normalized center-of-mass coordinate X may enter the channel wall area or fluctuate around the initial position. With increased inertia applied on the droplet, the normalized center-of-mass coordinate Y grows faster and the normalized center-of-mass coordinate Z decreases. It is found by the ULBM for the first time that the liquid droplet is pushed back into the GDL by inertial effect. With the increase of inertia and the decrease of contact angle of GDL, both the droplet penetration depth in GDL and the droplet invasion fraction increase. The droplet invasion fraction in GDL is up to 30%.     

Development of a procedure writers’ guide framework: Integrating the procedure life cycle and reflecting on current industry practices

The current study aims to advance a procedure development process that will create procedures as effective safeguards. Several past incidents identified that many current procedural systems failed to support workers conducting their work safely and effectively. There is currently a dearth of systematic inquiry regarding the procedure development process. A key element of the procedure development process is writers' guide, which dictates how these procedures should be developed, written, reviewed and managed. The current effort collected 16 writers' guides across various industries such as chemical, oil and gas, nuclear, and energy. Different components of these writers' guides were identified, summarized, and later categorized according to a newly developed writers' guide framework. The framework uses four phases of the procedure life cycle and five sections for procedure content. The analysis showed that industry practices primarily focus on general goals of the procedure, writing style, and review process. Many important components of the procedure, such as process hazard information, execution challenges, and training requirements were not addressed adequately. Of the 41 components, there were only 3 that 80% of the writers' guides contained suggesting little consensus about the content. The proposed writers' guide framework organizes all components including low frequency of appearance ones in different phases of procedure life cycle. The significance of the current procedure writers' guide framework is that it not only produces a structure for a comprehensive writers’ guide but also opens an opportunity for future improvement in current procedure writing practices.     

高速开关阀高频脉宽调制控制有效占空比工作范围的拓宽

高速开关阀在高频脉宽调制(Pulse width modulation,PWM)控制下阀芯可悬浮在某一开度,调节占空比即可改变阀口开度,实现对流量和压力的线性控制,因此在车辆控制系统得到广泛应用。但我国自主开发的高速开关阀PWM控制的有效占空比工作范围小,阀芯较易全开或全闭,为提高阀的可控性和控制精度,需要研究拓宽占空比的工作范围。基于汽车电子稳定程序(Electronic stability program,ESP)的高速开关阀,深入分析阀芯液动力的影响因素,应用AMESim、Matlab软件建立ESP液压系统的联合仿真模型,并经过试验验证,通过仿真得出阀座锥角、入口孔径对阀芯位移的影响,提出拓宽PWM控制占空比有效工作范围的关键参数,为高速开关阀的设计开发提供参考依据。     

Supervisory control strategies evaluated on a pilot Jameson flotation cell

An L-150 pilot Jameson flotation cell was instrumented and a distributed control system was developed. The parameters of a metallurgic phenomenological model were estimated from industrial data. A steady state simulator was built based on this nonlinear model.This hybrid system combines on-line measured operating variables with virtual variables, characterizing the feed. All these variables are fed on-line to a simulator to predict the characteristics of the concentrate and tailings.The expert system modifies the set points of the distributed control system, including two routines: expert feedback and feed forward control. Several cases for different feed conditions are discussed.     

洋麻纤维-棉纤维混纺织物/环氧树脂复合材料力学及吸湿性能

采用碱氧一浴法对洋麻纤维(KF)进行精细化处理,并制备了不同混纺质量比的精细化处理KF-棉纤维(KF-CF)混纺织物及KF-CF/环氧树脂(EP)复合材料。通过纤维强度、细度测试和FTIR、TG、SEM研究了精细化处理对KF性能的影响,通过对KF-CF/EP复合材料力学性能分析得到最佳混纺质量比,探究了最佳混纺质量比KF-CF/EP复合材料在湿热及化学环境下的吸湿性能。结果表明:精细化处理后的KF直径降低了30.66%,拉伸模量提高了31.24%,柔软度提高了13.20%,热稳定性得到提高;当KF与CF混纺质量比为40∶60时,KF-CF/EP复合材料力学性能最优,拉伸强度为101.90 MPa,弯曲强度为189.64 MPa;在湿热环境下,时间越长,温度越高,KF-CF/EP复合材料的吸水率越高,碱性环境会导致KF-CF/EP复合材料吸水率提高。      

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. In this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.     

Long-Time gap crowd prediction with a Two-Stage optimized spatiotemporal Hybrid-GCGRU

Crowd prediction is a crucial aspect of modern society, facilitating numerous decision-making processes, such as hazard detection and facility maintenance. Conventional crowd prediction relies on rule-based models which involve tedious formation steps and are error-prone. Hence, this study adopts spatiotemporal deep learning models for crowd prediction in order to exploits both temporal and spatial features of the dataset. There are three major limitations in previous studies: firstly, the prediction length of the time series models is relatively short (i.e., limited to single-digit time steps); Secondly, most studies using time series prediction do not consider the features in the neighboring locations other than the target location; Finally, only the temporal pattern on regular days is exploited, whilst the data variations during holidays can greatly reduce the prediction accuracy. Therefore, a particle swarm optimized (PSO) Hybrid-Graph Convolutional Gated Recurrent Unit (HGCGRU) model, entitled PSO-HGCGRU, is proposed to increase the prediction accuracy in various situations. The PSO-HGCGRU model is composed of two parts, 1) a hybrid model structure, which allows the model adapting to both regular and holiday features, and 2) the GCGRU model, in which the graph convolutional layer to extract the spatial features and the GRU layers to extract the temporal dependencies, to undertake spatiotemporal prediction. The Two-Stage Long-Time Gap Prediction method is applied to the proposed PSO-HGCGRU model with a newly designed two-stage mechanism to optimize the length of the time gap. Results show that the proposed model generally outperforms the baseline models by around 40% in accuracy.