In the rotary kiln alumina production process, because of the complexity and variability of rotary kiln burning zone conditions, some important quality index related process parameters can not be detected continuously on-line.Detecting the different burning zone conditions on-line is a key factor for the whole process automation of alumina industry. The current method depends on flame observation by naked eye.In order to realize automated recognition of burning zone conditions, a method which learned experience and knowledge from naked eye observation was proposed to recognize burning zone conditions by utilizing the image processing technique and pattern classification method.At first, features were extracted from flame images of rotary kiln burning zone and were combined with some important process parameters to constitute a hybrid feature vector.Then a model with a binary tree based SVM (support vector machine) was constructed.At last, a flame image recognition system was developed.The system was successfully applied to a domestic alumina plant, and good economic benefit was realized.
A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the systems cannot be linearized by feedback. The considered faults are modeled as both loss of effectiveness and lock-in-place （stuck at some unknown place）. It is proved that the proposed control scheme can guarantee all signals of the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error between the system output and the reference signal converge to a small neighborhood of zero, though the nonlinear functions of the controlled system as well as the actuator faults and the external disturbances are all unknown. Simulation results demonstrate the effectiveness of the control approach.
Because of the complexity,dynamicity and uncertainty of the steelmaking process,it is difficult to establish t...
Shengping Yu~1,Ruixia Lv~2,Binglin Zheng~1,Tianyou Chai~1 1.Key Laboratory of Process Industry Automation of Ministry of Education,Northeastern University,Shenyang,110004,China 2.Information Engineering Department,Yantai Automobile Engineering Professional College,Yantai,265500,China
Cluster tools have advantages of shorter cycle times,faster process development,and better yield for less contamination.The sequence of dual-arm cluster tools is a complex logistics process during the semiconductor production.Efficient use of cluster tools is naturally very significant to competitive fab operations.Generating an optimized sequence in a computationally efficient manner and assessing the quality of the requirements to improve the fab production are the key factors for semiconductor manufacturing productivity.The Petri net modeling is introduced to minimize the makespan of the process for the three different logical modes and select a better mode after comparing the makespan among the three logical modes.The tool sequence optimization problem is formulated as optimization firing transition sequences based on the Petri net and then the formulation is converted to be linearly solved by the branch-and-cut method in the standard commercial solver CPLEX.Special methods for the linear conversion are highlighted.Due to the limited calculation time requirement for the real production and the large scale of the problem,special methods for the efficiency tuning are applied according to the characteristics of the problem.Numerical testing is supported by one of the most advanced semiconductor enterprises and the computational results show significant improvement compared with the traditional manual sequence results.
