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Model-based control methods do not produce satisfactory control results with the batch process control of Czochralski (CZ) silicon monocrystalline with complex nonlinearity, large delay, and time-varying dynamics. Therefore, this paper proposes a data-driven model-free adaptive iterative learning control method (MFAILC) to achieve precision control of the batch process. Firstly, to improve the accuracy of the data-driven model, a novel deep learning model for crystal growth process is established by combining a stacked autoencoder (SAE) and a long short-term memory network (LSTM) to extract the working condition information and the dynamic timing features in process data. Traditional model-based control methods are limited by the difficulties in modeling and by the unmodeled dynamics. So, to overcome this problem, the heater controller and the crystal puller controller are designed, based on the iterative dynamic linearization technology, to ensure that the silicon monocrystalline batch manufacturing process always maintains precision control. Also, the discrete-time extended state observer (ESO) is introduced to compensate for the influence of disturbances, to improve the robustness of the control system. Finally, the efficacy of the proposed method is verified by applying it for the predictive modeling and batch control of thermal field temperature and crystal diameter during crystal growth. [ABSTRACT FROM AUTHOR]

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC. • A novel iterative learning MPC strategy which is achieved in a latent variable space is proposed for trajectory tracking and disturbance rejection of batch processes. • The advantages of latent variables in modeling and controller design are exploited through the implementation of an iterative learning MPC controller. • The convergence of LV-ILMPC is proven. • The proposed method is illustrated by its application in numerical simulations and a nonlinear boiler system. [ABSTRACT FROM AUTHOR]

Abstract Efficiency and accuracy are critical in the motion control of a batch process. This paper proposes a new intelligent motion control method for a batch process based on reinforcement learning (RL) and iterative learning control (ILC). The proposed learning-based motion control method enables the system to learn from its previous experience. The motion control method can be divided into two parts: (1) RL-based trajectory optimization and (2) ILC-based positioning control. Experiments were conducted to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method not only reduces the process time effectively while ensuring system stability, but also achieves excellent positioning accuracy. Highlights • A new intelligent motion control method for a batch process is proposed. • The obtained time-optimal trajectory reduces the process time by more than 35%. • The stability of the mechanism is guaranteed under the new trajectory. • Excellent positioning accuracy can be obtained under the new control method. • The proposed method can effectively handle the repeated and non-repeated disturbances. [ABSTRACT FROM AUTHOR]

The batch process is a batch-repeated production process, which shows a multiple modal switching within the batch. This makes it difficult to use a single-mode analysis method to achieve accurate modeling and fault diagnosis. Therefore, a novel two-step phase partition idea is proposed based on improved affinity propagation (AP) clustering and sub-phase similarity diminishing scan (PSDS) method. In order to capture the dynamic characteristics of the modes switching, the improved AP clustering is used for phase preliminary partition, in which an effective method that is more suitable for complex batch process is proposed to calculate the similarity. For sub-phases generated by the phase preliminary partition, the internal process of each sub-phase also varies obviously with the development of duration, so an innovative method PSDS is proposed to implement phase fine partition. Then each sub-phase scanned by the PSDS method is identified and divided into stable parts and transition parts, which further reflects the change trend within the sub-phase. For the outliers and misclassification points that may arise during the process of phase partition, the solutions are put forward, respectively. Thus, the partition results with different characteristics are modeled and monitored separately by using the method of principal component analysis (PCA). A practical application on batch process, aircraft steering engine platform fault diagnosis experiment, is given to conform the feasibility and performance of the proposed method. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Extension of stability criterion for reliable thermal runaway detection of batch processes. • Demonstration of the improvement over current criteria for process control. • Embedding of stability criteria within MPC algorithms. • Application of novel control scheme for the intensification of batch processes. Abstract Thermal runaways in batch processes can lead to significant issues for safety and performance during normal operation in industry. This is usually circumvented by running such processes at lower temperatures than necessary, hence losing the opportunity to intensify production and therefore reduce reaction time. The detection of the thermal stability of batch systems can potentially be embedded in an advanced control scheme, therefore improving the performance by being able to intensify the process, achieving higher yields while keeping a stable operation. The derivation of stability criterion K for high-order reactions is presented in this work, resulting in better control when embedded in Model Predictive Control (MPC) schemes than standard nonlinear MPC schemes, based on the work in Kähm and Vassiliadis (2018). The non-trivial extension of stability criterion K for multi-component reactions with application to MPC systems is discussed in detail. The logic and verification of the form of the resultant Damköhler number in particular is discussed and demonstrated with case studies. A comparison of various MPC schemes is presented, showcasing that the implementation using criterion K results in intensified processes kept stable at all times, whilst reducing computational cost with regard to standard nonlinear MPC schemes. Furthermore, reaction times are reduced by at least twofold with respect to processes run at constant temperatures. [ABSTRACT FROM AUTHOR]

Highlights • A two dimensional robust iterative learning fault-tolerant controller is presented. • Iterative learning control is incorporated into the established process model. • Average dwell time method and Lyapunov–Krasovskii function are applied to obtain system stability. Abstract Considering inevitable time delays, actuator faults and other issues in practical industrial batch processes, a two dimensional (2D) robust iterative learning fault-tolerant controller design is presented. The design consists of two parts. First, combining the state error and output error, the iterative learning control is incorporated into the established process model. Second, a switching control law is derived using switching system theory and the limited running time of batch processes. Based on the above two merits, the control law is designed using the sufficient condition that the system is robust stable where the switching control law will meet a lower bound. The advantages of the proposed method lie in the fact that time delay is incorporated in the batch direction during system modeling and traditional 1D problem has been extended to the 2D case, which provides the new idea for research on time delay systems. Moreover, it is shown that the lower bound of the switching control law is directly related to the convergence index in both time and batch directions, and a feasible design algorithm is offered in theory. Compared with the traditional 1D control that considers only the time direction, the system under the proposed 2D control law has a series of advantages such as fast convergence speed, good tracking performance and short running time. Finally, the validity of the proposed method is tested on the injection molding process in comparison with traditional 1D methods. [ABSTRACT FROM AUTHOR]

Highlights • The process is divided into different operation phases, and it is easy to carry out statistical analysis for each phase. • Global and local structures are fully extracted to preserve more features in each phase. • The probability density of the extracted structures is estimated to amplify useful information and suppress noise. Abstract A multi-phase batch process monitoring method based on multiway weighted global neighborhood preserving embedding (MWGNPE) is proposed. MWGNPE has three advantages. Firstly, for the multi-phase feature of batch process, gaussian mixture model (GMM) method is used to divide phases by clustering characteristics. Secondly, after the multiple phases have been divided, global and local structures are extracted by using global neighborhood preserving (GNPE) method. Thirdly, probability density estimation characteristic of GMM is introduced to estimate the probability density of the extracted global and local structures. It can amplify useful information and suppress noise. These three advantages make MWGNPE well suit for batch process monitoring. A full MWGNPE model is combined with the cluster and the density estimation characteristic of GMM concurrently to improve the effect of fault detection in batch process monitoring. The effectiveness and advantages of proposed method are verified by a numerical system and the penicillin fermentation process. The results show that the proposed method can effectively capture the fault information hidden in process data and has the superiority compared with other conventional methods. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) has been successfully applied to numerous batch processes over the past decades. Monotonic convergence of tracking error is a desired characteristic that attracts much attention in academia. Many factors arising in industrial practice, such as strong nonlinearity and parameter uncertainty, have challenged the most existing monotonically convergent ILC approaches. This motivates the development of the nonlinear monotonically convergent ILC (NMC-ILC) in this paper. The proposed NMC-ILC is an optimization-based control strategy, in which the original nonlinear process is linearly approximated to reduce the complexity of the optimization problem accompanied. An upper bound of the tracking error is derived by considering the effects of model mismatches that consist of two components: structural mismatch induced by the linearization and parametric mismatch arising from the indetermination of process parameters. The NMC-ILC is then devised by minimizing this upper bound. Numerical experiments are conducted on an ILC-testing benchmark and the velocity control in an injection molding process. [ABSTRACT FROM PUBLISHER]

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini–Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy. [ABSTRACT FROM PUBLISHER]

Support Vector Data Description (SVDD) is an efficient monitoring method that captures the spherically shaped boundary around the normal batch data and sets the control limit related to support vectors (SVs) for online monitoring. Using nonlinear transformation functions, SVDD constructs an irregular hypersphere in high dimensional space. When the batch process is complicated, the accuracy of monitoring will decrease with traditional control limit of SVDD. In this paper, dynamic hypersphere based support vector data description (DH-SVDD) is proposed for batch process monitoring. In training process, static hypersphere is built by the important SVs of training dataset. In testing process, dynamic hypersphere is built by the important SVs of combined dataset with current test sample and training dataset. If there is a significant change between these two hyperspheres, it means that the current test sample is an outlier. Thus, DH-SVDD has a relatively high monitoring accuracy because it fully considers relationship between the current test sample and the historical training dataset in high dimensional space. Comparison is conducted between the proposed DH-SVDD and traditional methods such as K-chart-SVDD, max limit SVDD and validation limit SVDD. The effectiveness of the DH-SVDD is also verified by a semiconductor etch process and a fed-batch penicillin fermentation process. [ABSTRACT FROM AUTHOR]

The protein A capture step is the main cost-driver in downstream processing, with high attrition costs especially when using protein A resin not until end of resin lifetime. Here we describe a feasibility study, transferring a batch downstream process to a hybrid process, aimed at replacing batch protein A capture chromatography with a continuous capture step, while leaving the polishing steps unchanged to minimize required process adaptations compared to a batch process. 35 g of antibody were purified using the hybrid approach, resulting in comparable product quality and step yield compared to the batch process. Productivity for the protein A step could be increased up to 420%, reducing buffer amounts by 30–40% and showing robustness for at least 48 h continuous run time. Additionally, to enable its potential application in a clinical trial manufacturing environment cost of goods were compared for the protein A step between hybrid process and batch process, showing a 300% cost reduction, depending on processed volumes and batch cycles. [ABSTRACT FROM AUTHOR]

In chemical process control, estimation of the process states, e.g. concentration or properties of the reactant or resultant, in real time is a key issue. Batch processes are typically characterized by unequal batch lengths and unsynchronized batch trajectories, posing challenges for the state estimation. Due to ignorance of such challenges, many existing methods would produce poor estimates in real applications. We design a new state estimation approach employing Bayesian filtering with consideration of batch-to-batch dynamics. To characterize the dynamics across batches with different time profiles, a synchronized two-dimensional (2-D) state-space model is constructed that contains synchronously equal- and unequal-length situations. Based on this model, a novel formulation of the particle filter is derived where the particles evolve along both the time and the batch dimensions so as to approximate the synchronized 2-D optimal estimates. Also, the convergence concern is addressed from a practitioner’s viewpoint. To incorporate appropriate data from previous batches into the current estimation, an on-line synchronization method based on the dynamic time warping technique is developed using a new alignment performance measure together with a transfer alignment strategy. The performance of the proposal is evaluated by case study on a numerical example and a three-state batch reaction process. [ABSTRACT FROM AUTHOR]

Effective integration and analysis of new high-throughput data, especially gene-expression and proteomic-profiling data, are expected to deliver novel clinical insights and therapeutic options. Unfortunately, technical heterogeneity or batch effects (different experiment times, handlers, reagent lots, etc.) have proven challenging. Although batch effect-correction algorithms (BECAs) exist, we know little about effective batch-effect mitigation: even now, new batch effect-associated problems are emerging. These include false effects due to misapplying BECAs and positive bias during model evaluations. Depending on the choice of algorithm and experimental set-up, biological heterogeneity can be mistaken for batch effects and wrongfully removed. Here, we examine these emerging batch effect-associated problems, propose a series of best practices, and discuss some of the challenges that lie ahead. [ABSTRACT FROM AUTHOR]

“Abnormal situation management” (ASM) in general refers to the various tasks required for online fault diagnosis and also hazard mitigation. Although quite a few ASM-related studies have already been carried out in the past, none of them addressed the wide range of issues consistently and rigorously with the same modeling tool. An automata-based strategy is therefore proposed in this work to synthesize all operating procedures needed for diagnostic tests and also other emergency response operations in the batch processes. The proposed model building techniques are suitable not only for characterizing all components in any given process, but also for representing the operation targets of all ASM tasks. Finally, notice that every resulting procedure can be readily expressed with an implementable sequential function chart (SFC). [ABSTRACT FROM AUTHOR]

Batch process is an important category of industrial processes. Recently, the combination of the real-time-feedback-based iterative learning control (ILC) and the model predictive controller (MPC) has demonstrated its advantage when applied to the batch process. In practical applications, the plants are always nonlinear and the model cannot be known exactly. Therefore, how to design such a control strategy for the constrained batch processes with unknown input nonlinearities and guarantee the convergence is interesting and valuable. Inspired by the dual-mode MPC, this paper proposes a two-mode framework for the constrained ILCâ€"MPC to solve this problem, which is constructed by a real-time-feedback-based strategy followed by a run-to-run strategy. Under the proposed framework, an invariant updating strategy based on the run-to-run strategy is developed for constrained batch processes to act as the second mode, which describes the situation of infinite batch and gives an estimation on the upper bound of the influence from the nonlinearity. Then, a real-time-feedback-based ILCâ€"MPC with a two-dimensional (2D) model is designed for the considered batch process, which adopts the time-varying horizon, and is proven to be recursively feasible and convergent. The case studies verify the results of the paper. [ABSTRACT FROM AUTHOR]

Solving a multi-input dynamic optimization of a batch processes is a complex problem involving interactions between input variables and constraints over time. The problem gets more difficult due to the presence of local solutions that have almost the same cost but widely varying structures. This paper studies various local optimal solutions for a non-isothermal semi-batch reactor with the feed rate and temperature as inputs and a heat removal constraint. Three solution patterns were studied, all consisting in meeting the heat removal constraint for the first part and seeking the compromise between the main and side reactions in the later part. A sensitivity analysis shows that the best solution pattern among those studied does not change with variations in parameters or initial conditions. [ABSTRACT FROM AUTHOR]

This work describes an application of Multivariate Statistical Process Control to monitor soybean oil transesterification. For the development of multivariate control charts, near infrared spectra were acquired in-line during the evolution of ten batches under Normal Operating Conditions. They were then organized in a three-way array (batch × spectral variable × time). This structure was analysed by the two most commonly used approaches to develop batch monitoring schemes for handling such kind of data, referred to as Nomikos-MacGregor (NM) and Wold-Kettaneh-Friden-Holmberg (WKFH), respectively. To assess the performance of the approaches, eight test batches, during which specific interferences were induced, were manufactured. When applied for off-line monitoring, both NM and WKFH correctly pointed out such intentionally produced failures. On the other hand, concerning on-line monitoring, NM exhibited a better fault detection capability than WKFH. Contribution plots were found to highlight the spectral region mostly affected by the disturbances regardless of the modelling strategy resorted to. [ABSTRACT FROM AUTHOR]

Traditional variables control charts assume that process data is independently identically normally distributed (IIND). Some real-world processes, however, do not obey this convenient assumption. The semiconductor industry is very familiar with non-IIND distributions. The batch processes in the semiconductor manufacturing involve nested variation sources. The variation sources are between-batch variation, the within-batch (or between-wafers) variation in each batch, and the within-wafer variation in each wafer. First, it is proved that the application of traditional mean control chart in the batch process would result in too many false alarms. In addition, some kinds of control charts proposed in previous studies either cannot be used to monitor all the three variation sources or contain too many control charts. In this paper, we propose a new control chart named as two-level nested control chart, composing of four control charts for synthetically monitoring the batch process, which can monitor all the three variation sources. Finally, it was proved by practical application in the process of phosphorus diffusion that this new control chart can be used to monitor all the three variation sources of batch processes. [ABSTRACT FROM AUTHOR]

A constrained latent variable model predictive control (LV-MPC) technique is proposed for trajectory tracking and economic optimization in batch processes. The controller allows the incorporation of constraints on the process variables and is designed on the basis of multi-way principal component analysis (MPCA) of a batch data array rearranged by means of a regularized batch-wise unfolding. The main advantages of LV-MPC over other MPC techniques are: (i) requirements for the dataset are rather modest (only around 10–20 batch runs are necessary), (ii) nonlinear processes can efficiently be handled algebraically through MPCA models, and (iii) the tuning procedure is simple. The LV-MPC for tracking is tested through a benchmark process used in previous LV-MPC formulations. The extension to economic LV-MPC includes an economic cost and it is based on model and trajectory updating from batch to batch to drive the process to the economic optimal region. A data-driven model validity indicator is used to ensure the prediction’s validity while the economic cost drives the process to regions with higher profit. This technique is validated through simulations in a case study. [ABSTRACT FROM AUTHOR]

In this paper, a window-based stepwise sequential phase partition method is proposed to improve monitoring performance for nonlinear batch processes with multiphase operations. The three-dimensional information matrix of batch operation is unfolded and normalized in the batch-wise direction to facilitate establishing the kernel principal component analysis (KPCA) models for phase partition. A moving window is introduced to improve the partition performance, with respect to the process dynamics and time sequence for operation. Consequently, phase partition algorithms are developed for even- and uneven-length batch processes, respectively. Moreover, a traversal algorithm is given to determine the optimal choice of the KPCA parameters and the window size for phase partition. A numerical case and two industrial multiphase processes of injection molding and penicillin fermentation are used to demonstrate the effectiveness and merit of the proposed phase partition method. [ABSTRACT FROM AUTHOR]

Batch process monitoring remains a challenging task due to the inherent time-varying dynamics. The occurrence of faults results in the variables having three types of variation behavior, that is, univariate variation without interaction effect, correlated variable variation with interaction effect, or all variable variation. The three behavior situations may change at any time under dynamic scenarios. Considering that an effective batch process monitoring method should clearly identify the complex variation behavior of the variables, a hierarchical support vector data description (HSVDD), which integrates univariate monitoring, subspace monitoring, and whole-space monitoring, is proposed to accurately identify the three variation behaviors. First, three-dimensional data are unfolded to batch-wise data with normalization, and the obtained two-dimensional data are separated according to the time axis to obtain time-slice modeling data. Then, the hierarchical structure, which contains the univariate variable monitoring structure layer, the subspace monitoring structure layer, and the whole space monitoring structure layer, is designed for each time slice. Meanwhile, support vector data description (SVDD) is adopted as the modeling method in each layer. Lastly, the online hierarchical monitoring model that has the same time point as the online data is selected, and the monitoring results of all models in the hierarchical structure are fused using two methods, that is, obtaining the average weight and the max value. A fault diagnosis method based on a hierarchical monitoring structure is designed. A simple numerical example and fed-batch penicillin fermentation process are employed to demonstrate the effectiveness of the HSVDD. [ABSTRACT FROM AUTHOR]

This paper deals with the design of feeding rates for dual-substrate fed-batch processes where the main control objective is the regulation of the microbial growth rate. To this end, feedback of the growth rate error is incorporated to a biomass proportional dual feeding law. A second-order sliding mode observer is used to estimate the growth rate, so that no additional sensors are required. Stability conditions are derived and robustness against several disturbances such as yield uncertainty, measurement errors and kinetic model mismatch is analytically and numerically evaluated. The advantages of the proposal include: minimal measurement requirements, regulation with fast convergence to the desired growth rate and reduced regulation error in the presence of disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we consider a nonlinear time-delay dynamic system with uncertain system parameters to characterize the process of batch fermentation. Our goal is to design an optimal control scheme to maximize the productivity of 1,3-propanediol (1,3-PD). Accordingly, we introduce an optimal control problem governed by the nonlinear time-delay dynamic system, in which the control variables are the free terminal time of the batch fermentation process and the initial concentrations of biomass and glycerol. The optimal control problem is subject to a cost sensitivity constraint for ensuring that an acceptable level of the system performance is achieved and continuous state inequality constraints for ensuring that the concentrations of biomass, glycerol, 1,3-PD, acetate, ethanol lie within specified limits. Then, the optimal control problem with free terminal time is transformed, via a hybrid time-scaling strategy, into an equivalent problem with fixed terminal time, which is much preferred for numerical computation. Using the constraint transcription and local smoothing approximation techniques, we approximate these continuous state inequality constraints by conventional inequality constraints to yield an approximate optimal control problem. Because of the highly complex nature of this approximate problem, a parallel algorithm based on the filled function method is constructed to solve this approximate problem. Finally, it is observed that the optimal control obtained is satisfactory through numerical simulations. [ABSTRACT FROM AUTHOR]

Iterative learning control is an effective control strategy for control of batch processes and initial condition is one of the most important factors affecting convergence of iterative learning batch process control. In this study, a novel initial value dynamic compensation‐based data‐driven optimal terminal iterative learning control (IDC‐DDOTILC) approach is proposed for non‐linear systems under random initial conditions. The unknown influence on the terminal output caused by the initial states is deduced by using a dynamical linearisation of the controlled non‐linear system along the iteration direction, and then the unknown influence is estimated iteratively and incorporated into the learning control law. As a result, the proposed IDC‐DDOTILC can drive the terminal output of the plant to attain the target value at the endpoint asymptotically under iteration‐varying initial conditions. Two chemical engineering examples including a batch reactor and a fed‐batch ethanol fermentation process are used to demonstrate effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Linear low density polyethylene (LLDPE) and high density polyethylene (HDPE) (with different shear thinning behaviors) were melt blended at two different compositions (10 and 25 wt%. of HDPE) and three different rotational speeds. Batch foaming process was performed over a wide range of temperatures. Bulk density measurement, cell structure evaluation, differential scanning calorimetry (DSC), and oscillatory rheology tests were performed. Results indicated the deterioration of foamability for those blended at the highest selected speed of 120 rpm. The blends mixed at a speed of 60 rpm, exhibited a slightly lower expansion ratio compared with those of mixed at a speed of 10 rpm with the same trend over the foaming temperature. For blends with 25 wt%. HDPE, proper cellular structures were obtained through a broader range of foaming temperature. [ABSTRACT FROM AUTHOR]

This paper studies an online identification algorithm for batch processes incorporating priori process knowledge of pole and zero positions. The knowledge is available to control engineers and can be exploited to improve the accuracy of the identified process model. To reduce the computation burden of directly invoking Lyapunov inequality, a bound on the identified parameters is imposed to enforce the match between the priori knowledge and identified model. The bound is recursively calculated according to the newly obtained model. The proposed identification method uses the information not only from the time direction but also along the batch direction to improve the identification performance from batch to batch. A filter is introduced to suppress the variation on the identified parameters. Finally, numerical simulations verify the performance and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Because of the multiplicity of operation phases in batch process, which have specific control objects, different dominant process variables and distinct process correlation characteristics, the faults may also have phase characteristic. To conduct fault diagnosis for batch process more precisely, this paper proposes a fault detection and diagnosis method based on fault feature phase identification results. Firstly, extreme learning machine is used to identify fault feature phases between the faulty data set and the normal data set. Then, focusing on the different data nature implied in different fault feature phases, several 'short stages' are partitioned for the whole batch. After that, different multiway fisher discriminant analysis (MFDA) models are developed for these 'short stages,' respectively. The proposed method can deepen the search space analyzed by fault diagnosis into specific fault feature phases, which not only overcome the disadvantage of too many models in MFDA, but also overcome the disadvantage of low diagnosis accuracy and high false recognition rate of traditional MFDA method. Simulation results show the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

In practice, batch processes mayhave different durations, revealingunsynchronized operation events, due to the changes in operation conditionsor control objectives. For batches with multiple operation phases,batch-wise process characteristics are irregular, and at the sametime, phases are misaligned over batches, which have caused problemsin phase analysis and modeling as well as online process monitoring.To solve the uneven-length problem in multiphase batch processes,this paper proposes a sequential time slice alignment based unequal-lengthphase identification and modeling method for fault detection of irregularbatches. In comparison with previous work, the major contributionof the proposed method is as follows: (1) The irregular process characteristicsare evaluated in sequence and directly related with the monitoringperformance. (2) Multiple irregular phases are readily identifiedand modeled using sequential time slice alignment which avoids cumbersomepostprocessing. (3) The sequential nature provides an easy way toreal-time judge the phase affiliation of each new sample for onlinefault detection. Also, comparison is conducted between the proposedalgorithm and clustering-based uneven-length phase division and processmonitoring algorithm. The application to a typical batch process withvarying durations illustrates the online monitoring performance ofthe proposed method. [ABSTRACT FROM AUTHOR]

A batchprocess can be viewed as a two-dimensional (2D) systemwith dynamics in both time and batch directions. To tackle these 2Ddynamics, many control algorithms have been proposed by combiningiterative learning control (ILC) with high-performance continuouscontrol algorithms under a 2D framework. However, these algorithmsrequire large computational load and memory, which are often demandingfor the software and hardware of the controllers in several fast batchprocesses. In this paper, a 2D predictive functional control algorithmthat combines ILC with predictive functional control (PFC) is proposed.Owing to the compactness and effectiveness of PFC, the proposed controlalgorithm can reduce the required computational load and memory. Otherthan the general form of the control law, two concise and practicalforms are provided as well. The proposed control scheme is testedthrough simulations and implementation in an injection molding process,which is a typical fast batch process. Results confirm the good performanceof the proposed control algorithm. [ABSTRACT FROM AUTHOR]

The objective of this research was to evaluate adsorption of heavy metals in single- and ternary-metal forms onto chicken bone biochar (CBB). Competitive sorption of heavy metals by CBB has never been reported previously. The maximum adsorption capacities of metals by CBB were in the order of Cu (130 mg g−1) > Cd (109 mg g−1) > Zn (93 mg g−1) in the single-metal adsorption isotherm and Cu (108 mg g−1) >> Cd (54 mg g−1) ≥ Zn (44 mg g−1) in the ternary-metal adsorption isotherm. Cu was the most retained cation, whereas Zn could be easily exchanged and substituted by Cu. Batch experimental data best fit the Langmuir model rather than the Freundlich isotherms. In the column experiments, the total adsorbed amounts of the metals were in the following order of Cu (210 mg g−1) > Cd (192 mg g−1) > Zn (178) in single-metal conditions, and Cu (156) > Cd (123) > Zn (92) in ternary-metal conditions. Results from both the batch and column experiments indicate that competitive adsorption among metals increases the mobility of these metals. Especially, Zn in single-metal conditions lost it adsorption capacity most significantly. Based on the 3D simulation graphs of heavy metals, adsorption patterns under single adsorption condition were different than under competitive adsorption condition. Results from both the batch and column experiments show that competitive adsorption among metals increases the mobility of these metals. The maximum metal adsorption capacity of the metals in the column experiments was higher than that in the batch experiment indicating other metal retention mechanisms rather than adsorption may be involved. Therefore, both column and batch experiments are needed for estimating retention capacities and removal efficiencies of metals in CBB. [ABSTRACT FROM AUTHOR]

In a standard optimization approach, the underlying process model is first identified at a given set of operating conditions and this updated model is, then, used to calculate the optimal conditions for the process. This “two-step” procedure can be repeated iteratively by conducting new experiments at optimal operating conditions, based on previous iterations, followed by re-identification and re-optimization until convergence is reached. However, when there is a model-plant mismatch, the set of parameter estimates that minimizes the prediction error in the identification problem may not predict the gradients of the optimization objective accurately. As a result, convergence of the “two-step” iterative approach to a process optimum cannot be guaranteed. This paper presents a new methodology where the model outputs are corrected explicitly for the mismatch such that, with the updated parameter estimates the identification and optimization objectives are properly reconciled. With the proposed corrections being progressively integrated over the iterations, the algorithm has guaranteed convergence to the process optimum and also, upon convergence, the final corrected model predicts the process behavior accurately. The proposed methodology is illustrated in a run-to-run optimization framework with a fed-batch bioprocess as a case study. [ABSTRACT FROM AUTHOR]

In the real world, there are a large number of supply chains that involve the short lifespan products. In this paper, we consider an integrated production and distribution batch scheduling problem on a single machine for the orders with a short lifespan, because it may be cheaper or faster to process and distribute orders in a batch than to process and distribute them individually. Assume that the orders have the identical processing time and come from the same location, and the batch setup time is a constant. The problem is to choose the number of batches and batch sizes to minimize the total delivery time without violating the order lifespan. We first give a backward dynamic programming algorithm, but it is not an actually polynomial-time algorithm. Then we propose a constant time partial dynamic programming algorithm by doing further research into the recursion formula in the algorithm. Further, using the difference characteristics of the optimal value function, a specific calculating formula to solve the problem with the setup time being integer times of the processing time is obtained. [ABSTRACT FROM AUTHOR]

We consider a single parallel-batching machine scheduling problem with delivery involving both batching scheduling and common due date assignment. The orders are first processed on the single parallel-batching machine and then delivered in batches to the customers. The batching machine can process several orders at the same time. The processing time of a production batch on the machine is equal to the longest processing time of the orders assigned into this batch. A common due date for all the orders in the same delivery batch and a delivery date for each order need to be determined in order to minimize total weighted flow time. We first prove that this problem is NP hard in the strong sense. Two optimal algorithms by using dynamic programming are derived for the two special cases with a given sequence of orders on the machine and a given batching in the production part, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the problem of economically oriented optimal operation of batch membrane diafiltration processes that are designed to concentrate the valuable components of the solution and to purge the impurities from it. We consider a complex economical objective that accounts for the total operational costs comprising a cost of consumed diluant, costs related to duration of processing, and a cost of product loss. The optimization problem is formulated as a multi-objective optimal control problem in order to investigate the impact of operational cost factors on optimal operation policy. This is achieved thanks to the use of the analytical approach that exploits Pontryagin's minimum principle. We show that the economically optimal control strategy is to carry out an operation involving saturated (bang-bang or constraint-tracking) control modes and a singular arc. For the most common cases of diafiltration problems, it turns out that the switching of the consecutive control modes can be realized in the state feedback fashion, i.e. the entire optimal operation is defined analytically in the space of process states. We demonstrate the applicability of the presented approach and we illustrate achievable benefits, over traditional control methods for the batch diafiltration processes, on two case studies taken from the literature. [ABSTRACT FROM AUTHOR]

In batch statistical process control (BSPC), data from a number of “good” batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA). In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data. The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as ‘batch maturity index’) from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations. The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other hand were shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox. [ABSTRACT FROM AUTHOR]

In order to meet tight product quality specifications for batch/semi-batch processes, it is vital to monitor and control product quality throughout the batch duration. The ideal strategy is to achieve end-product quality specifications through trajectory tracking control during a batch run. However, due to the lack of in situ sensors for continuous monitoring of batch product quality, the measurements are usually implemented by laboratory assays and are inherently intermittent. Therefore, direct trajectory tracking of batch product quality is challenging in such applications. This paper proposes a practical approach to realise trajectory tracking control of batch product quality in those situations where only intermittent measurements are available. The first step of the approach consists in identifying a projection to latent structures (PLS) model to identify a relationship between readily measured process variable trajectories and intermittently measured batch product quality. Then the identified PLS-based prediction model is transformed into recursive formulation by utilising missing data imputation algorithms. Such recursive formulation allows identified PLS-based model to be readily incorporated as a predictor into standard model predictive control (MPC) framework. Case study employing simulated fed-batch fermentation process used to manufacture penicillin was employed to illustrate the principle and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions. [ABSTRACT FROM AUTHOR]

The quality of product in industrial process can be improved by on-line monitoring, regulating and correcting actions. There are two approaches addressing this problem: (1) Statistical Process Control (SPC), which focuses on identifying assignable causes that can be removed, leads to permanent process improvement or reduction in variability; and (2) Engineering Process Control (EPC), based on adjusting the process variables to get less deviation from target (often called feedback adjustment). Feedback adjustment regulates the process to account for sources of variability that cannot be removed by the SPC approach. Integrating SPC/EPC is a very effective way in quality control, since features from both SPC and EPC could give a complementary performance. This work introduces a framework that integrates SPC and EPC in one methodology; SPC control limits for critical key variables are developed depending on information from the historical reference of past successful processes. While the EPC algorithm is derived of a progressive set of knowledge-based rules. The approach is applied to data collected from chlorine industry, as a case study. This is poorly automated, subject to several disturbances, monitored by measuring the feed brine solution concentration and the acidity index of samples from the ferric brine treatment tank. The experimental results proved that, when the production process is affected by certain disruptions, the process engineers have a decision making tool, by on-line monitoring and regulating the process key variables. Furthermore, by implementing proper adjustment strategies, the stability of the process can be better maintained, and significant economic benefits will be achieved. [ABSTRACT FROM AUTHOR]

Highlights: [•] A state space model predictive fault-tolerant control for batch processes with partial actuator faults is proposed. [•] The design is applied to injection velocity control. [•] Improved closed-loop control performance is achieved with unknown disturbances and actuator faults. [ABSTRACT FROM AUTHOR]

SUMMARY Based on recent papers that have demonstrated that robust iterative learning control can be based on parameter optimization using either the inverse plant or gradient concepts, this paper presents a unification of these ideas for discrete-time systems that not only retains the convergence properties and the robustness properties derived in previous papers but also permits the inclusion of filters in the input update formula and a detailed analysis of the effect of non-minimum-phase dynamics on algorithm performance in terms of a 'plateauing' or 'flat-lining' effect in the error norm evolution. Although the analysis is in the time domain, the robustness conditions are expressed as frequency domain inequalities. The special case of a version of the inverse algorithm that can be used to construct a robust stable anti-causal inverse non-minimum-phase plant is presented and analysed in detail. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigated different optimizing schemes for an industrial polymerization. [•] Two implementations are considered, an optimizing NMPC controller and a simplified optimizing controller. [•] The simplified scheme combines a model-predictive temperature controller and a feed controller in a cascaded fashion. [•] We show by simulations that both schemes achieve the goals of tight temperature control and reduction of the batch time. [•] The cascaded scheme could be directly implemented into the DCS of the plant and is in daily operation. [Copyright &y& Elsevier]

Actuatorfaults commonly exist in process control systems. Dueto these faults, the controller may not achieve the required target,so the control performance may degrade. In this paper, the fault-tolerantcontrol is designed through a predictive functional control frameworkto deal with partial actuator faults and unknown disturbances, whichexist widely in process control systems. Based on a new state spaceformulation of the process models, an improved predictive functionalcontrol scheme is proposed, where satisfactory closed-loop controlperformance is achieved even with unknown disturbances and actuatorfaults. With the actuator faults, the system becomes a process withparameter uncertainties. Hence, a sufficient condition that guaranteesclosed-loop robust stability is presented. Simulations are given toillustrate the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In a practical multiphase batch process,the durations of batchesare probably different, and at the same time, the length of each phasemay also vary from batch to batch. This paper proposes an effectivemethod to handle the uneven length problem in multiphase batch processes,which is also useful when the number of training batches is limited.To address the data nonlinearity, two nonlinear modeling techniquesare introduced. For online monitoring and quality prediction, a keystep is how to locate the current data sample to the specific phase,on which basis an appropriate model can be employed. In this paper,a phase discrimination model for data localization in different phasesthat is based on the support vector data description model is developed.For performance evaluation of the proposed method, detailed illustrationsof a typical multiphase batch process are provided. [ABSTRACT FROM AUTHOR]

ABSTRACT On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] A real vehicle routing problem with backhaul, time window and multiple batches is considered. [•] The problem includes vehicle assignment to overlapped batches and vehicle routing in batch. [•] We develop two tabu search algorithms to handle the problem. [•] We study the algorithm efficiency, tradeoff between traveling cost and customer service. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose an MPC strategy for batch processes with hard constraints on model validity. [•] Hard constraints ensure proper use of the model which improves prediction accuracy. [•] Better predictions lead to improved control results in MPC. [•] The proposed strategy is easy to deploy as no initial trial and error of the tuning parameters is needed. [•] Provided additional case-specific tunning parameters further improves the control results. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-block progressive modelling approach is proposed for enhanced fault isolation in batch processes. [•] Through progressive modelling and contribution analysis, variables related to or affected by the fault, as well as the associated time information, are gradually identified. [•] Fault propagation paths can be established through multi-block progressive modelling. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel performance monitoring method for batch processes is proposed. [•] Mutual information between measurement and quality variables is maximized within kernel feature subspaces. [•] New monitoring indices are developed for quality relevant nonlinear and non-Gaussian batch processes. [•] The application results demonstrate the superiority of the proposed approach over the conventional methods. [ABSTRACT FROM AUTHOR]

The main control goal in batch process is to get a high yield of products. In this paper, to maximize the yield of 1,3-propanediol (1,3-PD) in bioconversion of glycerol to 1,3-PD, we consider an optimal control problem involving a nonlinear time-delay system. The control variables in this problem include the initial concentrations of biomass and glycerol and the terminal time of the batch process. By a time-scaling transformation, we transcribe the optimal control problem into a new one with fixed terminal time, which yields a new nonlinear system with variable time-delay. The gradients of the cost and constraint functionals with respect to the control variables are derived using the costate method. Then, a gradient-based optimization method is developed to solve the optimal control problem. Numerical results show that the yield of 1,3-PD at the terminal time is increased considerably compared with the experimental data. [ABSTRACT FROM AUTHOR]

Abstract: Operating cost of a batch process can be reduced using multiple resources. In this paper, a methodology is proposed to target multiple resources in order to minimize the operating cost of the overall batch process. The proposed methodology is based on the concept of prioritized cost, originally developed for continuous processes. Prioritized cost of a resource depends on the pinch quality, quality of the resource and its cost. The concept of prioritized cost is extended to optimize the operating cost of a batch process. It is proved that to minimize the operating cost a batch process, prioritized cost of a resource in any time interval may not necessarily depends on the pinch quality of that interval. Prioritized cost of a resource in any time interval depends on the highest pinch quality of all the subsequent intervals, including itself. This important result is proved mathematically. The proposed methodology is applicable to fixed-scheduled, fixed-flow batch processes involving single quality. [Copyright &y& Elsevier]