Based on the proportional-integral (PI) closed-loop control widely used in industrial engineering practice, a robust iterative learning control (ILC) method is proposed for industrial batch processes subject to time-varying uncertainties and load disturbance. An important merit is that the proposed ILC design is independent of the PI tuning which maintains the closed-loop system stability, owin...

This document describes the genotyping analysis methodology used with data produced by the DMETTM Plus Product. This method is designed to analyze samples in one of two ways: single-sample (individual) analysis, and dynamic clustering analysis. Single-sample analysis produces conservative, accurate results that do not depend on any additional data within a batch of experiments. When single-samp...

This paper explains an implementation model for a new method for Production Leveling designed for batch production system. The main structure of this model is grounded on three constructs: traditional framework for Operations Planning, Lean Manufacturing concepts for Production Leveling and case study guidelines. By combining the first and second construct, a framework for Production Leveling h...

This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batchwisely, and then a nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80L SBR for biological wastewater treatment, where the SBR poses an inte...

Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of SOM-based method for the analysis of Expandable PolyStyrene (EPS) batch process. To this end, a data set of EPS-batch process was used for training a SOM. Reference vectors of the SOM were then classified by K-means algorithm into six clusters, which represent product t...

Learning is a flexible and effective means of extracting the stochastic structure of the environment. It provides an effective method for blind separation and deconvolution in signal processing. Two different types of learning are used, namely batch learning and on-line learning. The batch learning procedure uses all the training examples repeatedly so that its performance is compared to the st...

We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is ...

Typical statistical machine translation systems are batch trained with a given training data and their performances are largely influenced by the amount of data. With the growth of the available data across different domains, it is computationally demanding to perform batch training every time when new data comes. In face of the problem, we propose an efficient phrase table combination method. ...

Background: Interferon α-2b is a vital biotherapeutic produced through the recombinant DNA technology in E. coli. The recombinant IFN-α2b normally appears as intercellular IBs, which requires intensive refolding and purification steps. Method: Purification of IFN-α2b from solubilized IB was performed using two-phase extraction. To optimize refolding conditions, the effects of pH and different a...

 It is well-known fact that batch processes are gaining wider ground in chemical industries. Compared with continuous processes the control of batch processes is more difficult because physical and chemical properties of the contents, such as heat capacity, heat transfer coefficient and reaction rate vary from run to run and within runs. The control problem focuses on the temperature control o...