Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Janus particles by simplified RAFT-based emulsion polymerization process for polymer coating

We describe a simplified method to synthesize film forming polymer Janus particles by phase separation during RAFT-based free radical emulsion polymerization. Fully crosslinked snowman- or football-shaped polystyrene Janus particles (PSJPs) were first produced in a one-step batch process using amphiphilic triblock macro-RAFT copolymers as stabilizers. Such particles were in turn employed as seeds in a continuous emulsion polymerization in which a monomer mixture of methyl methacrylate (MMA) and butyl acrylate (BA) (1/1 by weight) was constantly injected into the reaction in the presence of a water soluble initiator. The added monomers wetted seed particle surface and their polymerization led to formations of 93-nm film forming single- or two-headed Janus particles. The resulted latex was successfully used to disperse and encapsulate solid calcite extender. Graphical abstract

Enhanced l-Malic Acid Production by Aspergillus oryzae DSM 1863 Using Repeated-Batch Cultivation

l-Malic acid is a C4-dicarboxylic acid and a potential key building block for a bio-based economy. At present, malic acid is synthesized petrochemically and its major market is the food and beverages industry. In future, malic acid might also serve as a building block for biopolymers or even replace the commodity chemical maleic anhydride. For a sustainable production of l-malic acid from renewable resources, the microbial synthesis by the mold Aspergillus oryzae is one possible route. As CO2 fixation is involved in the biosynthesis, high yields are possible, and at the same time greenhouse gases can be reduced. In order to enhance the production potential of the wild-type strain Aspergillus oryzae DSM 1863, process characteristics were studied in shake flasks, comparing batch, fed-batch, and repeated-batch cultivations. In the batch process, a prolonged cultivation time led to malic acid consumption. Keeping carbon source concentration on a high level by pulsed feeding could prolong cell viability and cultivation time, however, did not result in significant higher product levels. In contrast, continuous malic acid production could be achieved over six exchange cycles and a total fermentation time of 19 days in repeated-batch cultivations. Up to 178 g/L l-malic acid was produced. The maximum productivity (0.90 ± 0.05 g/L/h) achieved in the repeated-batch cultivation had more than doubled than that achieved in the batch process and also the average productivity (0.42 ± 0.03 g/L/h for five exchange cycles and 16 days) was increased considerably. Further repeated-batch experiments confirmed a positive effect of regular calcium carbonate additions on pH stability and malic acid synthesis. Besides calcium carbonate, nitrogen supplementation proved to be essential for the prolonged malic acid production in repeated-batch. As prolonged malic acid production was only observed in cultivations with product removal, product inhibition seems to be the major limiting factor for malic acid production by the wild-type strain. This study provides a systematic comparison of different process strategies under consideration of major influencing factors and thereby delivers important insights into natural l-malic acid production.

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

Kernel Fisher Envelope Surface for Pattern Recognition

It is found that the batch process is more difficultly monitored compared with the continuous process, due to its complex features, such as nonlinearity, non-stable operation, unequal production cycles, and most variables only measured at the end of batch. Traditional methods for batch process, such as multiway FDA (Chen 2004) and multi-model FDA (He et al. 2005), cannot solve these issues well. They require complete batch data only available at the end of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.

Soft-Transition Sub-PCA Monitoring of Batch Processes

Batch or semi-batch processes have been utilized to produce high-value-added products in the biological, food, semi-conductor industries. Batch process, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes than that of continuous processes, due to the common natures of batch process: non-steady, time-varying, finite duration, and nonlinear behaviors. The lack of exact monitoring model in most batch processes leads that an operator cannot identify the faults when they occurred. Therefore, effective techniques for monitoring batch process exactly are necessary in order to remind the operator to take some corrective actions before the situation becomes more dangerous.