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.

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.

Immobilization of Aspergillus oryzae DSM 1863 for l-Malic Acid Production

Whole-cell immobilization by entrapment in natural polymers can be a tool for morphological control and facilitate biomass retention. In this study, the possibility of immobilizing the filamentous fungus Aspergillus oryzae for l-malic acid production was evaluated with the two carbon sources acetate and glucose. A. oryzae conidia were entrapped in alginate, agar, and κ-carrageenan and production was monitored in batch processes in shake flasks and 2.5-L bioreactors. With glucose, the malic acid concentration after 144 h of cultivation using immobilized particles was mostly similar to the control with free biomass. In acetate medium, production with immobilized conidia of A. oryzae in shake flasks was delayed and titers were generally lower compared to cultures with free mycelium. While all immobilization matrices were stable in glucose medium, disintegration of bead material and biomass detachment in acetate medium was observed in later stages of the fermentation. Still, immobilization proved advantageous in bioreactor cultivations with acetate and resulted in increased malic acid titers. This study is the first to evaluate immobilization of A. oryzae for malic acid production and describes the potential but also challenges regarding the application of different matrices in glucose and acetate media.

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.

Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring

In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.

Robust Asynchronous Fuzzy Predictive Fault-Tolerant Tracking Control for Nonlinear Multiphase Batch Processes with Time-Varying Tracking Trajectories

A T-S model-dependent robust asynchronous fuzzy predictive fault-tolerant tracking control scheme is developed for nonlinear multiphase batch processes with time-varying tracking trajectories and actuator faults. Firstly, considering the influence of the mismatch between the previous phase state and the current phase controller during the switching, a T-S fuzzy switching model including the match and mismatch case is established. Depending on the fuzzy switching model, a robust fuzzy predictive fault-tolerant tracking controller is designed by considering the situation whether the model rules correspond to the controller rules. Secondly, using the related theories and methods, the system stability conditions presented by the linear matrix inequality considering the above conditions are provided to guarantee the stability of the system. By solving these stability conditions, the T-S fuzzy control law gain of each phase, the minimum running time of each match case and the maximum running time of each mismatch case are obtained. Then, through the maximum running time, the strategy of the switching signal in advance is adopted to avoid the occurrence of asynchronous switching, so as to ensure that the system can operate stably. Finally, the simulation results verify that the designed controller is effective and feasible.

Predicting black soldier fly larvae biomass and methionine accumulation using a kinetic model for batch cultivation and improving system performance using semi-batch cultivation

Global demand for poultry and associated feed are projected to double over the next 30 years. Insect meal is a sustainable alternative to traditional feeds when produced on low-value high-volume agricultural byproducts. Black soldier fly (BSF) larvae (Hermetia illucens L.) are high in protein and contain methionine, an essential amino acid that is critical to poultry health. BSF larvae can be grown on many organic residues, however, larvae growth and quality vary based on feedstock and cultivation processes. Experiments were completed to monitor temporal changes in BSF larvae growth and composition using almond hulls as a growth substrate under batch and semi-batch processes and with varying substrate carbon to nitrogen ratio (C/N). A logistic kinetic growth model was developed to predict larval biomass and methionine accumulations during batch production. Estimated ranges of model parameters for larvae maximum specific growth rate and carrying capacity were 0.017–0.021 h−1 and 9.7–10.7 g larvae kg−1 hulls dry weight, respectively. Methionine content in larvae increased from 11.1 to 17.1 g kg−1 dry weight over a 30-day batch incubation period. Larvae-specific growth and yield increased by 168% and 268%, respectively, when cultivated in a semi-batch compared to a batch process. Increasing C/N ratio from 26 to 40 increased density of methionine content in larvae per unit feedstock by 25%. The findings demonstrate a logistic model can predict larvae biomass accumulation, harvest time can achieve specific methionine contents, and a semi-batch process is more favorable for larvae biomass accumulation compared to a batch process.