For control and optimization of chemical processes, the traditional hierarchical control structure (HCS), where an optimizer in the real-time optimization (RTO) layer updates the set-points of controlled variables (CVs) in the lower control layer, has been well-acknowledged and widely adopted in industrial applications. However, a common drawback of such an HCS is that the speed for a plant to converge to an optimal operation is slow because the optimizer has to wait for the process to settle from one steady-state to another to get an accurate disturbance estimation before making any changes to the set-points. In this Article, a novel HCS based on the concepts of controlled variable adaptation (CVA) and nonoptimality detection is proposed. In the CVA strategy, the CVs are determined and adapted on the basis of a so-called just-in-time regression algorithm to approximate the necessary conditions of optimality (NCO), which makes the self-optimizing performance adaptive to operating condition changes. For nonoptimality detection, we apply the theory of statistical process monitoring to monitor the optimality of process operation, where the nonoptimal statuses are treated as a special kind of process faults. The detection results are used as a prerequisite to activate the CVA. With these techniques, the proposed CVA-based HCS exhibits the following distinct features: (1) The regulatory control layer has an ability to approach a near optimal operation automatically through selfoptimizing control, thus accomplishing the majority of the optimization task, and the speed of the process converging to an optimal status is fast. (2) The RTO layer extends the self-optimizing operation range via adapting CVs in the lower control layer, rather than their set-points as in a traditional HCS. (3) The activation of CVA is neither regular nor periodic, but only evoked when it is necessary. Two case studies are provided to demonstrate the basic characteristics and advantages of the proposed CVA-based HCS.
The distance calculation between two photovoltaic arrays is important in the design of grid-connected and grid-off power generation. It is easy to calculate the distance between two photovoltaic arrays on horizontal ground, but on the sloping ground existed in practical projects, it is more complicated. This paper introduces a kind of analytical geometry method to solve the problem of distance calculation between two photovoltaic arrays fixed on sloping ground.
To implement the quality prediction scheme for batch processes, long short-term memory (LSTM) neural network is a feasible tool to handle with the process dynamics and nonlinearity. However, a global LSTM soft sensor suffers a decline in performance facing batch-to-batch variations. To overcome the batch diversity problem and take advantage of LSTM model, a multivariate trajectory based ensemble just-in-time learning strategy is proposed in this paper. Different trajectory based similarity measurements are designed to extract historical batch trajectories which share similar spatial positions and trends. For each selected trajectory, an online local LSTM soft sensing model is constructed and the real-time quality prediction result for each local model can be obtained. Then, a weighting parameter is determined for each model by cross validation. Bringing together quality prediction results from different local models, the ensemble prediction result can be finally figured out. Two case studies are carried out to prove the effectiveness of the proposed methodology including a fed-batch reactor and the fed-batch penicillin fermentation process.INDEX TERMS Batch production systems, Ensemble just-in-time learning, long short-term memory, multivariate trajectory analysis, soft sensor, quality prediction.
This paper deals with the run‐to‐run optimization problem of batch processes in the presence of uncertainty with a tailored self‐optimizing control (SOC) strategy. Firstly, the dynamic programming problem for the batch process is transformed into a static nonlinear programming (NLP) problem using the control parameterization method. Then combinations of output measurements are selected as controlled variables (CVs), which are batch‐wise controlled to account for uncertainties. However, although existing SOC methods appear directly applicable to such a static NLP formulation, a major problem therein is that the number of control parameters is generally large to maintain a satisfactory optimizing performance, which makes them inappropriate as being manipulated variables for closed‐loop optimization. To circumvent this difficulty, it is proposed to alternatively use the so‐called latent effective manipulated variables as the control system's manipulated variables, which are linear combinations of original control parameters, however, less in number whilst implicitly dominating optimal operation in the whole uncertain space. This way, the run‐to‐run self‐optimizing control system is designed with less process‐dependent CVs and operated with minimal complexity. A simulated fed‐batch reactor is provided to illustrate the proposed methodology.
In order to reduce energy consumption and improve the stability of cement burning system production, it is necessary to conduct in-depth analysis of the cement burning system, control the operation state and law of the system. In view of the rotary kiln consumes most of the fuel, we establish the simulation model of the cement kiln used to find effective control methods. It is difficult to construct mathematical model for the rotary cement kiln as the complex parameters, so we expressed directly using neural network method to establish the simulation model for the kiln. Choosing reasonable state and control variables and collecting actual operation data to train neural network weights. We first in-depth analyze mechanism and working parameters correlation to determine factors of the yield and quality as the model input variables; then constructed cement kiln model based on BP and Elman network, both achieved good fitting results. Elman network model has a faster convergence speed, high precision and good generalization ability. So the Elman network based model can be used as simulation model of the cement rotary kiln for exploring new control method.
In this paper, we consider iterative learning control for trajectory tacking of robotic manipulator with uncertainty. An improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic manipulator. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. To deal with uncertainty, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are incorporated for estimation of uncertain parameters by constructing extended system states. The performances between the two filters are also compared. Simulations on a 2DOF Robot manipulator demonstrate that the improved Q-ILC with parameter estimators can achieve faster convergence and better transient performance compared to the original Q-ILC, in the presence of measurement noise and model uncertainty.
Abstract-The effectiveness of the flipped classroom in college education is largely improved by combined with smart learning strategies recent years. However, most traditional smart learning methods focus on the use of smart devices in the learning step and ignore the in-classroom application. Meanwhile, with the appearance of "Internet plus education", more and more online education platforms are available for mobile devices. To meet the increasing demand of the flipped classroom based on the online education; this study aims at extending the area of smart learning into the in-class study. A novel smart learning based education paradigm is proposed by re-designing the structure of the traditional flipped classroom. In the new teaching structure, the performance of the flipped classroom can be improved obviously since smart learning strategies be come the key element which spreads through all the procedure. For performance evaluation, a preliminary practice of a college course has been conducted and the result shows the advantage of the proposed paradigm.
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