A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Some remarks on the reactor network synthesis for gold cyanidation

Gold cyanidation in most of the plants occurs in a cascade of large tanks because this configuration offers the longest residence time and consequently the highest gold recovery. However, the long contact between the ore and the leaching solution can increases the reactants consumption by side reactions, and in some cases this conventional reactor configuration will not be the most suitable. In this paper, several configurations for a gold leaching circuit with five equal-sized reactors are investigated and the effect of different network configurations on the process performance addressed. It is showed that, for high gold content and low cyanide concentration the conventional in-series configuration is the most recommended; however, for high cyanide concentration and low gold content the in-parallel configuration is the best one. For intermediate values of gold content and cyanide concentration, hybrid configurations with parallel sections at the beginning of the circuit are the best choice. It is also showed that the optimal circuit configuration strongly depends on the gold price and cyanide cost, and for low values of cyanide price the conventional in-series configuration is the most recommended, while for high cyanide prices the hybrid and parallel configurations must be taken into account.     

Multiplicity of steady states in FCC units: effect of operating conditions

The FCC dynamics has been studied to determine the effect of operating conditions on multiplicity of steady states. The proposed model is capable to analyze several dynamic features to characterize the FCC process. Three combustion modes are analyzed: Partial combustion, total combustion and transition mode. At least three steady states have been determined in both partial and complete combustion modes. Furthermore, it is established in this paper that operating conditions are incapable to change the number of steady states. It is believed that a similar analysis should be applied to identify the effect on the multiplicity of physical parameters such as oxidation rates.     

Stability analysis and antiwindup design of uncertain discrete-time switched linear systems subject to actuator saturation

This paper investigates the stability analysis and antiwindup design problem for a class of discrete-time switched linear systems with time-varying norm-bounded uncertainties and saturating actuators by using the switched Lyapunov function approach.Supposing that a set of linear dynamic output controllers have been designed to stabilize the switched system without considering its input saturation,we design antiwindup compensation gains in order to enlarge the domain of attraction of the closed-loop system in the presence of saturation.Then,in terms of a sector condition,the antiwindup compensation gains which aim to maximize the estimation of domain of attraction of the closed-loop system are presented by solving a convex optimization problem with linear matrix inequality(LMI)constraints.A numerical example is given to demonstrate the effectiveness of the proposed design method.     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

Adaptive synchronization of master-slave large-scale systems against bias actuators and network attenuations

This paper utilizes the adaptive technique to design a class of active synchronizing masterslave large scale systems against imperfect actuators and networked interconnections with bias faults and signal attenuations, respectively. Without the requirement of knowledge of eventual faulty factors of bias-actuators on systems, and attenuation factors of connected networks, an adaptive mechanism is designed to estimate each unknown faulty factor on-line for constructing a class of distributed adaptive controllers. Then based on the adaptive adjustment parameters and Lyapunov stability theory, Lyapunov functions are addressed to prove that the proposed adaptive master-slave large-scale system can be guaranteed to be asymptotic synchronization with the improper actuator and faulty transmitted signals. Finally, a multiple vehicle large-scale system is used to verify the efficiency of the method.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Data-based adaptive online prediction model for plant-wide production indices

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.     

随机环境下带有私人信息的单机能力分配策略

本文研究随机环境下考虑私人信息的单机能力分配问题.该问题中各部门的单位产品收益、需求信息及加工设备各时段的能力为私人信息,且各时段中产品的加工时间为随机变量.本文采用设备方与各部门协商的方法对问题进行求解,首先利用随机规划理论将能力分配问题清晰化,然后利用拉格朗日松弛和泰勒级数展开方法进行协商机制设计,接着给出基于偏转次梯度方法的协商参数更新法则,最后综合上述过程给出最终的问题求解算法.数值算例验证了算法的有效性并分析了关键参数对能力分配结果的影响.     

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H_∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H_∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.