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The build-up rate of the bottom hole assembly (BHA) fluctuates violently during the deviation correction in the vertical drilling process due to the mutation of stratum, low operation accuracy of the BHA, etc. As a result, the uncertainty of the build-up rate of the BHA has great influence on the performance of correction control and the quality of the drilling trajectory. To deal with this control problem, a tube-based adaptive model predictive control (MPC) method is developed in this article. By dividing the trajectory extension model into two lower order models, the calculability of the disturbance invariant set of the tube-based MPC controller is ensured. Meanwhile, the bootstrap sampling method with a sliding window is used to estimate the range of the uncertainty online to reduce the conservativeness of the controller based on practical drilling data. Moreover, rules and a small interim deviation are introduced to adjust the parameters adaptively to decrease the curvature of the drilling trajectory and the calculation time. Simulations and hardware-in-the-loop experiment tests are carried out to illustrate the effectiveness of the control method. The results show that the proposed method completes the drilling task with a higher quality of trajectory and needs less time for calculation. [ABSTRACT FROM AUTHOR]

This paper is concerned with the correction of trajectory deviation in vertical drilling. Note that the accuracy of correction control will be reduced significantly by measurement and process noises, which finally leads to that the inclination angle exceeds beyond a tolerable limit. To deal with such noises and take into account practical constraints, a deviation correction strategy is developed for vertical drilling based on particle filtering and improved model predictive control in this paper. Firstly, the distributions and characters of the measurement and process noises in vertical drilling process are analyzed, and their approximate prior probability distributions are obtained. Based on the analysis, the structure of the deviation correction strategy is provided, including a particle filter and an improved model predictive controller which introduces a flexible constraint and an adjustable weight. The particle filter is effective to reject the measurement noises, and the improved model predictive controller plays an important role in achieving a small inclination of the drilling trajectory. Finally, two groups of simulations are carried out to illustrate the effectiveness of the proposed correction strategy. • A correction strategy is developed for vertical drilling subject to the noises. • A particle filter is introduced to acquire quality parameters for the controller. • A flexible constraint is used to ensure feasibility of MPC during correction. • An adjustable weight is proposed to change the priority of control appropriately. [ABSTRACT FROM AUTHOR]

Proper burden distribution is an important way to improve conditions of a blast furnace, which indicate the stability and energy consumption. However, it is difficult for operators to recognize the stability and energy consumption at the same time and adjust them through burden distribution, due to complex iron-making mechanism. Therefore, this article presents a two-stage decision-making method for the burden distribution parameters based on recognizing the conditions. First, the stability and energy consumption are recognized, respectively, according to the classification of stability of slag crust (SSC) and the gas utilization rate (GUR). And then, two prediction models are presented for the SSC and GUR to predict the conditions. Finally, the burden distribution parameters are determined in two stages, where edge parameters of the burden distribution are determined at first according to the SSC, and then, center parameters are determined according to the GUR. Experimental results based on industrial data show that the proposed method provides an effective decision-making way for improving the conditions. [ABSTRACT FROM AUTHOR]

Accurate prediction of carbon efficiency is a prerequisite for achieving energy saving and consumption reduction in an iron ore sintering process, and is the key to guaranteeing the quality and yield of sintered ore. This paper proposes an original real-time dynamic prediction model for carbon efficiency prediction in the process. A Savitzky–Golay filter is used to eliminate noise of the actual production data collected from a cooperative sintering plant, and the correlation between carbon efficiency and process parameters is determined by mutual information. A modified version of maximum entropy clustering algorithm is presented for identifying working conditions to accurately discriminate between anomalies and normal working conditions. Then, the real-time dynamic prediction model of carbon efficiency based on broad learning is established by taking into account the process characteristics and using the prediction error information under normal working conditions. The proposed model is demonstrated to be valid by carrying out some experiments with actual production data. The experimental comparative analysis show that this model has good generalization capabilities and high real-time prediction accuracy, and is superior to other advanced methods in dynamic prediction of carbon efficiency. • An improved maximum entropy clustering is presented to identify working conditions. • A real-time dynamic prediction model of CCR based on broad learning is established. • A suitable modeling framework is proposed to reflect the sintering process dynamics. [ABSTRACT FROM AUTHOR]

Accurately predicting the burn-through point (BTP) is crucial for achieving stable control of the sintering process. However, accurately measuring the raw BTP is difficult due to the harsh production environment and poor thermocouple measurement accuracy of the temperature of exhaust gas in bellows. This paper proposes a prediction model of the BTP with data correction based on the feature matching of cross-section frame at discharge end. Firstly, a feature extraction method of cross-section frames at discharge end is designed. Next, the cross-section frame at discharge end features matching method is used to correct the raw BTP, and this method corrects anomalous data resulting from sensor failures. Finally, the temporal convolutional neural network and gated recurrent unit are used to predict the corrected BTP. The prediction model considers the cross-section frame feature at discharge end and state parameters as inputs, and it can achieve accurate prediction of the corrected BTP. A series of comparative experiments are conducted to verify the feasibility and effectiveness of the proposed model. At the same time, this paper also designs industrial implementation plan,and use actual operation data to verify the feasibility of the designed industrial implementation plan. • A BTP correction method based on cross-section frame feature matching is proposed. • A BTP prediction model with data correction using TCN-GRU is proposed. • The designed industrial implementation scheme realizes the application of the model. [ABSTRACT FROM AUTHOR]

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process. • A fuzzy time series prediction model with the Fuzzy C-Means clustering is designed. • A hybrid prediction model of the burn-through point is developed. • The proposed model has a higher prediction accuracy than the existed models. [ABSTRACT FROM AUTHOR]