Performance monitoring of a multi-product semi-batch process

Traditionally principal components analysis (PCA) has been viewed as a single-population method. In particular in multivariate statistical process control, PCA has been used to monitor single product production. An extension to principal components analysis is presented which enables the simultaneous monitoring of a number of product grades or recipes. The method is based upon the existence of a common eigenvector subspace for the sample variance–covariance matrices of the individual products. The pooled sample variance–covariance matrix of the individual products is then used to estimate the principal component loadings of the multi-group model. The methodology is applied to a semi-discrete industrial batch process manufacturing a number of recipes. The industrial application illustrates that the detection and diagnostic capabilities of the multi-group model are comparable to those achieved by developing a separate statistical representation for the individual products.     

一种多产品多约束混合生产线的批量计划模型及其求解

从烧碱以及PVC两条生产线的生产过程提炼出多产品多约束混合生产线批量计划问题。我们针对该问题建立了以产品利润最大化为目的,包含资源、生产工艺和能力等约束的批量计划优化模型。通过微粒群算法进行优化,其计算模型及结果对于工业生产的实际情况具有一定的参考价值。     

Coordination and control of batch-based multistage processes

Run-to-run (R2R) process control has attracted much attention in research and has been widely used in practice. It has been proved effective at compensating for process disturbances by using R2R controllers at a single stage. However, most manufacturing processes span across multiple stages; variation in earlier stages can be magnified stage by stage if they are not properly eliminated. In addition, products are processed batch by batch in certain manufacturing processes. In such cases, the traditional EWMA controller might not effectively reduce the variation. This paper focuses on developing a process control strategy for batch production in a multistage process. In the newly proposed framework, a batch-allocation operation is introduced to group products into similar clusters before each stage; an R2R controller is then implemented to generate customized recipes for each batch. This framework emphasizes better coordination among the stages in a multistage process. Simulation results show that the proposed strategy is effective for the reduction of variation.     

Region fuzzy control for batch processes Part 2. Feed timing prediction and control for an antibiotic fermentation production process

Whether a selection of a feed timing for an antibiotic fermentation production process is suitable or not will directly influence the performance of the process. A prediction and decision model of feed timings for an industrial acetyl spiramycin fermentation production process is established using the approach of the region fuzzy control for batch processes. The results of industrial tests, carried out with the established model, are satisfactory.     

基于自适应多向独立成分分析的间歇过程监控的研究

针对间歇过程批次与批次之间,操作条件缓慢变化的特性,提出一种基于自适应多向独立成分分析(MICA)的监控算法。该方法首先用MICA法建模,然后在历史数据集中加入新的正常批次并剔除最早批次,逐渐更新模型,同时引入遗忘因子,提高对新过程特性的适应性。青霉素发酵过程的仿真结果表明,自适应MICA比MICA更准确地描述过程行为,并有效减少检测故障时的误报。     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

A new object model of batch equipment and procedural control for better recipe reuse

Application of the ISA-88 standard in industrial batch-process control often leads to repetition of information in recipes and to a low level of their reuse. This problem stems from the deficiencies of the standard batch-process control object model. A solution to the problem is proposed that is based on a more sophisticated object model of equipment and procedural control, with dynamically defined and potentially overlapping unit classes. The new concept, together with its elements, is described, and its use is illustrated and validated by means of a real batch control project. The validation is carried out as a comparison of the number of master recipes and unit procedures created under the new object model with the number of master recipes and unit procedures needed to perform the same functionality using the standard object model. The comparison demonstrates that the proposed approach has a significant advantage.     

Stability and performance analysis of mixed product run-to-run control

Run-to-run control has been widely used in batch manufacturing processes to reduce variations. However, in batch processes, many different products are fabricated on the same set of process tool with different recipes. Two intuitive ways of defining a control scheme for such a mixed production mode are (i) each run of different products is used to estimate a common tool disturbance parameter, i.e., a “tool-based” approach, (ii) only a single disturbance parameter that describe the combined effect of both tool and product is estimated by results of runs of a particular product on a specific tool, i.e., a “product-based” approach. In this study, a model two-product plant was developed to investigate the “tool-based” and “product-based” approaches. The closed-loop responses are derived analytically and control performances are evaluated. We found that a “tool-based” approach is unstable when the plant is non-stationary and the plant-model mismatches are different for different products. A “product-based” control is stable but its performance will be inferior to single product control when the drift is significant. While the controller for frequent products can be tuned in a similar manner as in single product control, a more active controller should be used for the infrequent products which experience a larger drift between runs. The results were substantiated for a larger system with multiple products, multiple plants and random production schedule.     

间歇发酵过程放罐时机的启发式模糊识别与预报

间歇发酵过程放罐时机的优化是提高产量,产率和降低能耗的一个重要方面.本文运用 模糊集理论,并将统计学方法和人的经验合为一体,提出了一种放罐时机的启发式模糊识别、 预报的建模方法.并且,根据一实际工业抗生素生产的历史资料和现场的分析,运用本文提出 的方法建立了该过程放罐时机的识别与预报模型.同时构造了计算机时实识别系统,几批工 业实验的结果表明,模型预报的效果良好.     

Tool path programming optimization for incremental sheet forming applications

Incremental sheet forming is an emerging process to manufacture sheet metal parts that is well adapted for small batch production or prototypes. The adjustment time is short, as it is sufficient to modify the tool motions to optimize the manufacturing process. Tool path generation therefore becomes a key topic linked to incremental sheet forming, and process characteristics ask for dedicated tool paths. Hence, this paper first discusses the impact of tool path types and other programming parameters on process implementation through an experimental campaign performed on a parallel kinematics machine tool. Then, a new approach to generate and control Intelligent CAM programmed tool paths is proposed. The major purpose of this innovative concept is to use process constraints for programming and controlling the tool path, which are adapted during the running of the CNC program according to real-time process data evaluation. Validation studies and an industrial implementation are finally presented to assess the efficiency of the proposed approach.     

Experiences in batch trajectory alignment for pharmaceutical process improvement through multivariate latent variable modelling

The primary objective of batch data as trajectory alignment (or synchronization) is to standardize the data sampling per batch according to the evolution of the process, and secondarily to homogenize the samples per run. The use of an indicator variable performs both objectives well. Two examples from the pharmaceutical sector are discussed to illustrate the different ways to deal with uneven samples across batches and across variables in the same batch. Since trajectory alignment requires large time investment, a simple triage approach is proposed to assess the need to analyze the dynamics of a given process and hence perform alignment. The presented examples are representative of a broad variety of batch processes that are operated by recipe in the pharmaceutical sector. In our experience, the variables associated with the automation triggers in these recipes are the best indicator variables to use for alignment. This is due to (i) the fact that the trigger variables are easy to identify from the automation of the recipe, (ii) operators are familiar with these, (iii) the target values for triggers are known a priori and hence the resulting alignment scheme can be performed in real-time for monitoring applications and (iv) it makes the monitoring scheme easy to understand and justify around the design-space since the design-space may originally be defined in terms of the trigger variables for each phase of the batch.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

一种间歇过程异常数据剔除的主元分析方法

提出一种用于间歇生产过程中异常数据控制的方法。这种方法将原始的三维间歇生产数据集合展开成一个二维数据矩阵,进行中心化和规格化后再转化成另一个按照时间序列排列的二维数据矩阵。这种方法可以克服Wold方法在对数据进行中心化时引起的原始信息失真问题。通过对聚合反应釜过程数据进行分析,表明该方法能有效地对生产数据剔除异常。     

Product identification in industrial batch fermentation using a variable forgetting factor

For reliable operation and the optimization of production, industrial fermentation processes require appropriate tools for monitoring the process in real time. This work presents the structure and operation of a soft sensor for the on-line monitoring of biomass and product concentration during salinomycin and bacitracin fermentation in an industrial, 80-m³ batch reactor; moreover it provides a tool for evaluation of batch production verified in industrial application. The process estimation algorithm consists of decoupled growth and product models, which ensures an unbiased convergence of the estimator and the robustness of the model. The production of secondary metabolites is described with a non-structured model upgraded with a variable forgetting factor that demonstrated a successful estimation of the non-measured parameters and states of this highly interactive and interlinked system with complex dynamics. The possibility of using various input signals in product identification yields independent soft sensors. This serves to improve the reliability of the predictions, mutual sensor control and enables the detection of irregularities in the fermentation process before the broth becomes useless.     

Reflective workpiece detection and localization for flexible robotic cells

A smart vision system for industrial robotic cells is presented. It can recognize and localize a reflective workpiece, and allows for automatic adjustments of the robot program. The purpose of the study is to improve industrial robots awareness of the environment and to increase adaptability of the manufacturing processes where full control over environment is not achievable. This approach is particularly relevant to small batch robotic production, often suffering from only partial control of the process parameters, such as the order of jobs, workpiece position, or illumination conditions.A distinguishing aspect of the study is detection of workpieces made of diverse materials, including shiny metals. Reflective surfaces are common in the industrial manufacturing, but are rarely considered in the research on object recognition because they hinder many of the object recognition algorithms. The proposed solution has been qualified and tested on a selected benchmark in a realistic workshop environment with artificial light conditions. The training of the object recognition software is an automatic process and can be executed by non-expert industrial users to allow for recognition of different types of objects.     

Batch process monitoring with tensor factorization

A batch process monitoring method using tensor factorization, tensor locality preserving projections (TLPP), is proposed. In many existing vector-based methods on batch process monitoring such as MPCA and MLPP, a batch data is represented as a vector in high-dimensional space. But vectorizing batch data will lead to information loss. Essentially, a batch data is presented as a second order tensor, or a matrix. In this case, tensor factorization may be used to deal with the two-way batch data matrix directly instead of performing vectorizing procedure. Furthermore, tensor representation has some advantages such as low memory and storage requirements and less estimated parameters for normal operating condition (NOC) model. On the other hand, different from principal component analysis (PCA) which aims at preserving the global Euclidean structure of the data, the TLPP aims to preserve the local neighborhood information and to detect the intrinsic manifold structure of the data. Consequently, TLPP may be used to find more meaningful intrinsic information hidden in the observations. The effectiveness and advantages of the TLPP monitoring approach are tested with the data from a benchmark fed-batch penicillin fermentation and two industrial fermentation processes, penicillin and cephalosporin, respectively.     

Data-driven,using design of dynamic experiments,versus model-driven optimization of batch crystallization processes

A new data-driven experimental design methodology, design of dynamic experiments (DoDE), is proposed as a means of developing a response surface model that can be used to effectively optimize batch crystallization processes. This data-driven approach is especially useful for complex processes for which it is difficult or impossible to develop a knowledge-driven model in a timely fashion for the optimization of an industrial process. Design of dynamic experiments [1] generalizes the formulation of time-invariant design variables from design of experiments, allowing for consideration of time-variant design variables in the experimental design. When combined with response surface modeling and an appropriate optimization algorithm, a data-driven optimization methodology is produced, which we call DoDE optimization. The method is used here to determine the optimal cooling rate profile, which integrates to give the optimum temperature profile, for a batch crystallization process. To examine the effectiveness of the DoDE optimization method, the data-driven optimum temperature profile is compared to the optimum temperature profile obtained using a model-based optimization technique for the potassium nitrate–water batch crystallization model developed by Miller and Rawlings [2]. The temperature profiles calculated using DoDE optimization yield response values within a few percent of the true model-based optimum values. A sensitivity analysis is performed on one case study to evaluate the distribution of the response variable from each method in the presence of parameter and initial seed distribution variability. It is demonstrated that there is partial overlap in the distributions when only variability in the model parameters is evaluated and there is substantial overlap when variability is included in both the model and initial seed distribution parameters. From this evidence, it can be concluded that the DoDE optimization method has the potential to be a useful data-driven optimization tool for batch crystallization processes where a first-principles model is not available or cannot be developed due to time and/or cost constraints.     

Object-oriented and distributed approach for programming robotic manufacturing cells

Flexible manufacturing systems (FMS) are essential for small/medium batch and job shop manufacturing. These types of production systems are used to manufacture a considerable variety of products with medium/small production volumes. Therefore, the manufacturing platforms supporting these types of production must be flexible and organized in flexible manufacturing cells (FMC). Programming FMCs remains a difficult task and is an actual area of research and development. This paper reports an object-oriented approach developed for FMC programming. The work presented was first thought for application in industrial robot manipulators, and later extended to other FMC equipments just by putting the underlying ideas in a general framework. Initially, the motivation for this work was to develop means to add force control to a standard industrial robot manipulator. This problem requires remote access to the robot controller, remote programming and monitoring, as also is required to program and monitor any other FMC equipment. The proposed approach is distributed based on a client/server model and runs on Win32 platforms, i.e., Microsoft Windows and Windows NT. Implementation for the special case of industrial robot manipulators is presented, along with some application examples used for educational, research and industrial purposes.     

Scenario-integrated on-line optimisation of batch reactors

A key problem area in recipe design for exothermic batch reactors is the possible occurrence of failure situations, in particular malfunctions of the cooling system. Scenario-integrated optimization has recently been developed in order to tackle these problems rigorously. This paper extends the ideas presented earlier to the on-line solution of scenario-integrated optimization problems. Due to high computational requirements, the problems are formulated only for special cases and on short time horizons. It is shown that the resulting MPC scheme can indeed optimize batch reactor recipes while simultaneously guaranteeing the enforcement of constraints, both for nominal operation as well as for failure situations. A literature example and an industrial polymerization reactor are treated to illustrate these properties.     

Modelling real world industrial systems: an inter-disciplinary approach based on petri nets

A modelling approach that will facilitate an in-depth understanding of the inter-relations of the different phenomena, human interactions and environmental factors constituting “real world” industrial processes is presented. Important industrial systems such as aluminium smelters, nuclear plants and batch chemical reactors have inter-related internal process activities coexisting with external events and requires an inter-disciplinary approach to model them. This modelling framework is based on identifying as modules, processes prevalent in industrial systems which to some degree are homogeneous in their actions. The selected initial set of modules are structured as petri net models and made to interact iteratively to provide process states of the system. The goal is accomplished by identifying the evolution of the process states and interpreting this in a markov chain as a means of effective representation of the “actual running” of the industrial process. The paper discusses the function and the implementation of the modelling method as applicable to the industrial reduction (smelting) of aluminium.