Batch Chemical Processes Research Articles

Modeling of batch processes using explicitly time-dependent artificial neural networks.

A neural network architecture incorporating time dependency explicitly, proposed recently, for modeling nonlinear nonstationary dynamic systems is further developed in this paper, and three alternate configurations are proposed to represent the dynamics of batch chemical processes. The first configuration consists of L subnets, each having M inputs representing the past samples of process inputs and output; each subnet has a hidden layer with polynomial activation function; the outputs of the hidden layer are combined and acted upon by an explicitly time-dependent modulation function. The outputs of all the subnets are summed to obtain the output prediction. In the second configuration, additional weights are incorporated to obtain a more generalized model. In the third configuration, the subnets are eliminated by incorporating an additional hidden layer consisting of L nodes. Backpropagation learning algorithm is formulated for each of the proposed neural network configuration to determine the weights, the polynomial coefficients, and the modulation function parameters. The modeling capability of the proposed neural network configuration is evaluated by employing it to represent the dynamics of a batch reactor in which a consecutive reaction takes place. The results show that all the three time-varying neural networks configurations are able to represent the batch reactor dynamics accurately, and it is found that the third configuration is exhibiting comparable or better performance over the other two configurations while requiring much smaller number of parameters. The modeling ability of the third configuration is further validated by applying to modeling a semibatch polymerization reactor challenge problem. This paper illustrates that the proposed approach can be applied to represent dynamics of any batch/semibatch process.

Regularization‐based statistical batch process modeling for final product quality prediction

Prediction accuracy and model interpretation are two important aspects with regard to regression models. In the field of statistical modeling of chemical batch processes, most research focuses on prediction accuracy, while the importance of the latter aspect is often overlooked. In multiphase batch processes, it is possible that only a few phases are relevant to certain quality indices, while different time points belonging to the same relevant phase usually have similar contribution to the quality. The regression coefficients of batch process model should reflect such process characteristics, that is, the coefficients corresponding to the irrelevant phases should be close to zero, while the coefficients of each variable within the same phase should vary smoothly. In this study, regularization techniques are introduced to statistical modeling of chemical batch processes to achieve both accurate prediction and good interpretation. The application to an injection molding process shows the feasibility of the proposed methods. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2815–2827, 2014

Semi-supervised mixture discriminant monitoring for chemical batch processes

In order to ensure operation safety and consistent product quality, multivariate statistical methods have been widely adopted in chemical batch process monitoring. In this paper, a semi-supervised mixture discriminant monitoring (SMDM) scheme is proposed, which integrates the strengths of both supervised and unsupervised techniques. The semi-supervised characteristic enables SMDM to fully make use of both labeled and unlabeled data, leading to more reliable process models. In addition, SMDM is suited to handling non-Gaussian distributed data that are commonly observed in batch processes. Inheriting from supervised learning, SMDM has better online fault diagnosis capability of known faults compared to the unsupervised multivariate statistical process monitoring methods. Meanwhile, the utilization of control charts makes SMDM capable to detect unknown faults. After an unknown fault is detected, the process variables most contributing to the fault can be identified through missing variable analysis. Such information is valuable for process engineers to find out the root cause of the fault. The collected data of the new faults are then used to update the monitoring model. By doing so, the fault diagnosis performance of the monitoring model can be improved online. The proposed method is demonstrated through its application to an injection molding process.

ChemInform Abstract: Aerobic Copper‐Catalyzed Organic Reactions

AbstractReview: 1557 refs.

Integrated Scheduling and Dynamic Optimization of Complex Batch Processes with General Network Structure Using a Generalized Benders Decomposition Approach

We address the integration of scheduling and dynamic optimization for batch chemical processes. The processes can have complex network structures, allowing material splitting and mixing. The integrated problem is formulated as a mixed-integer dynamic optimization problem where a continuous-time scheduling model is linked to the dynamic models via processing times, processing costs, and batch sizes. To reduce the computational complexity, we develop a tailored and efficient decomposition method based on the framework of generalized Benders decomposition by exploiting the special structure of the integrated problem. The decomposed master problem is a scheduling problem with variable processing times and processing costs, as well as the Benders cuts. The primal problem comprises a set of separable dynamic optimization problems for the processing units. By collaboratively optimizing the process scheduling and the process dynamics, the proposed method substantially improve the overall economic performance of the batch production compared with the conventional sequential method which solves the scheduling problem and the dynamic optimization problems separately. In comparison with the simultaneous method which solves the integrated problem by a general-purpose MINLP solver directly, the proposed method can reduce computational times by orders of magnitude.

Dense CdS thin films on fluorine-doped tin oxide coated glass by high-rate microreactor-assisted solution deposition

Continuous microreactor-assisted solution deposition is demonstrated for the deposition of CdS thin films on fluorine-doped tin oxide (FTO) coated glass. The continuous flow system consists of a microscale T-junction micromixer with the co-axial water circulation heat exchanger to control the reacting chemical flux and optimize the heterogeneous surface reaction. Dense, high quality nanocrystallite CdS thin films were deposited at an average rate of 25.2nm/min, which is significantly higher than the reported growth rate from typical batch chemical bath deposition process. Focused-ion-beam was used for transmission electron microscopy specimen preparation to characterize the interfacial microstructure of CdS and FTO layers. The band gap was determined at 2.44eV by UV–vis absorption spectroscopy. X-ray photon spectroscopy shows the binding energies of Cd 3d3/2, Cd 3d5/2, S 2P3/2 and S 2P1/2 at 411.7eV, 404.8eV, 162.1eV and 163.4eV, respectively.

Discriminating between critical and non-critical disturbances in (bio-)chemical batch processes using multi-model fault detection and end-quality prediction

This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided.

Model-Based Identification and Analysis of the Energy Saving Potential in Batch Chemical Processes

Optimization of energy consumption for reducing the relevant costs and environmental impacts is constantly gaining attention in chemical batch production. Existing methodologies focus on heat integration considering scheduling constraints and typically result in trade-offs between capital investment and operational costs. However, in multipurpose batch plants, even the allocation of energy flows and the consistent operation according to production recipes pose a great challenge due to batch-to-batch variability and lack of energy utility consumption meters. This paper utilizes a bottom-up modeling approach for energy utility consumption and proposes a method for model-based identification of the energy saving potential in chemical batch plants. The bottom-up models can accurately track the energy utility consumption at various production levels and are used as soft sensors for energy efficiency analysis studies. In this context, a set of energy key performance indicators (EKPIs) is proposed for quantifying efficiency in energy use, and an energy saving potential index (ESPI) based on historical plant performance serves as a shortcut method in the case of missing or inaccurate production recipes. The methodology is applied to an industrial multipurpose batch plant for specialty chemicals, exemplifying the obtained efficiency results and targeting energy saving potential for steam consumption.

Improvements to multivariate data analysis and monitoring of batch processes by multilevel methods

Batch process data contain between‐run and within‐run sources of variation, which complicates data analysis and process monitoring. Multilevel methods, such as multilevel simultaneous component analysis, greatly enhance the interpretation of large sets of batch process data by separating the between‐run and within‐run variation. Using a multilevel approach in batch process monitoring greatly reduces the occurrence of false alarms that are caused by trivial variations and process changes that were made on purpose. This also reduces the need for model updating, which often is a large effort and causes downtime of the monitoring system. Furthermore, relevant phenomena that are masked by larger but uninteresting sources of variation when standard batch process monitoring methods are applied can often be detected if the multilevel approach is used. Chemical batch process data are used to illustrate the methods. Copyright © 2012 John Wiley & Sons, Ltd.

DEMINERALISASI DAN DEPROTEINASI KULIT UDANG SECARA KONTINYU PADA TAHAPAN EKSTRAKSI KITIN SECARA BIOLOGIS

Chitin extraction in industry has been conducted by chemical process. The process has been known as a harsh treatment that badly affected to chitin quality, equipment and the environment. Since the last decade biologically chitin extractionhas more attracted attention. The biologically chitin extraction was conducted by batch fermentation or subsequent-batch fermentation. Continous demineralization and deproteinization is a new inovation on biologically chitin production technology.This system promises as an alternative technology for overcoming problems of batch fermentation process and chemical process. The objectives of the experiment was to obtain the optimal condition for continous deminineralizationand deproteinization for producing chitin from Panaeus vannamei shrimp shells. Lactobacillus acidophilus FNCC 116 and Bacillus licheniformis F11.1 was used for demineralization and deproteination process respectively. The results showed that the best condition for continuous demineralization was 6,5% glucose feed, with 16 hours retention time. For continuous deproteinization, the best condition was with 12 hours retention time. The process could remove 92.95% ash and 91.40% protein. The chitin, ash, and protein content of chitin product was 96.69%, 1.44% and 1,76% respectively.

Dynamic model-based fault diagnosis for (bio)chemical batch processes

To ensure constant and satisfactory product quality, close monitoring of batch processes is an absolute requirement in the (bio)chemical industry. Principal Component Analysis (PCA)-based techniques exploit historical databases for fault detection and diagnosis. In this paper, the fault detection and diagnosis performance of Batch Dynamic PCA (BDPCA) and Auto-Regressive PCA (ARPCA) is compared with Multi-way PCA (MPCA). Although these methods have been studied before, the performance is often compared based on few validation batches. Additionally, the focus is on fast fault detection, while correct fault identification is often considered of lesser importance. In this paper, MPCA, BDPCA, and ARPCA are benchmarked on an extensive dataset of a simulated penicillin fermentation. Both the detection speed, false alarm rate and correctness of the fault diagnosis are taken into account. The results indicate increased detection speed when using ARPCA as opposed to MPCA and BDPCA at the cost of fault classification accuracy.

An automata-based approach to synthesize untimed operating procedures in batch chemical processes

Systematic synthesis of untimed operating procedures has always been considered as an important design issue for batch chemical processes. An automaton-based method is developed in the present study to perform this task automatically. On the basis of the proposed methodical model-building principles, two distinct types of automata can be constructed to characterize the plant behaviors and control specifications, respectively. An admissible supervisor can be produced by applying the parallel composition operation with these models. For the purpose of identifying the most efficient operation procedures, the supervisor can then be integrated with a set of auxiliary automata to set the operation target(s) and, also, to impose upper limits on the total numbers of actuator actions and operation steps. Three examples are presented to demonstrate the feasibility and correctness of the proposed approach.

Measurement noise influence on statistical properties of batch-end quality predictions

In this paper, an extensive Monte Carlo simulation is performed to investigate the influence of output measurement noise on Multiway Partial Least Squares (MPLS) batch-end quality predictions. MPLS models are well suited for monitoring (bio)chemical batch processes, but the lack of insight in noise influence leaves companies reluctant to accept the technique. Simplified relations between prediction variance and measurement noise exist for spectroscopy calibration problems, but are based on assumptions that do not necessarily hold for batch process modelling. The non-linear properties of the PLS predictor and the lack of knowledge about its statistical distribution make the derivation of an analytical relation extremely difficult.Based on an extensive case study of a penicillin production process, MPLS predictions of final batch quality are shown to outperform offline quality measurements. Even at very high noise levels, the models capture the important information in the measurements and discard most of the noise. Prediction bias and variance are studied and found to behave inversely with respect to the model order. This inverse behaviour has important consequences for model order selection, which becomes a trade-off between bias and variance. In this light, several crossvalidation-based techniques for selection of the optimal number of principal components are compared. An adjusted Wold's R criterion proves to be slightly favorable to the minimum MSE and general Wold's R criterion.

Green chemistry and engineering: a practical design approach

Green chemistry and engineering: a practical design approach

Process systems engineering: From Solvay to modern bio- and nanotechnology.: A history of development, successes and prospects for the future

The term Process Systems Engineering (PSE) is relatively recent. It was coined about 50 years ago at the outset of the modern era of computer-aided engineering. However, the engineering of processing systems is almost as old as the beginning of the chemical industry, around the first half of the 19th century. Initially, the practice of PSE was qualitative and informal, but as time went on it was formalized in progressively increasing degrees. Today, it is solidly founded on engineering sciences and an array of systems-theoretical methodologies and computer-aided tools. This paper is not a review of the theoretical and methodological contributions by various researchers in the area of PSE. Its primary objective is to provide an overview of the history of PSE, i.e. its origin and evolution; a brief illustration of its tremendous impact in the development of modern chemical industry; its state at the turn of the 21st century; and an outline of the role it can play in addressing the societal problems that we face today such as; securing sustainable production of energy, chemicals and materials for the human wellbeing, alternative energy sources, and improving the quality of life and of our living environment. PSE has expanded significantly beyond its original scope, the continuous and batch chemical processes and their associated process engineering problems. Today, PSE activities encompass the creative design, operation, and control of: biological systems (prokaryotic and eukaryotic cells); complex networks of chemical reactions; free or guided self-assembly processes; micro- and nano-scale processes; and systems that integrate engineered processes with processes driven by humans, legal and regulatory institutions. Through its emphasis on synthesis problems, PSE provides the dialectic complement to the analytical bent of chemical engineering science, thus establishing the healthy tension between synthesis and analysis, the foundation of any thriving discipline. As a consequence, throughout this paper PSE emerges as the foundational underpinning of modern chemical engineering; the one that ensures the discipline's cohesiveness in the years to come.

An automaton-based approach to evaluate and improve online diagnosis schemes for multi-failure scenarios in batch chemical processes

Online diagnosis has been considered as an important measure for improving operational safety in many batch chemical plants. Specifically, the state transition behaviors of all hardware items (components) in the given batch process and their failure mechanisms are modeled systematically with automata. The system model is then assembled by connecting the component models on the basis of a generic hierarchical structure. A “diagnoser” can be constructed accordingly for the purpose of determining various qualitative and quantitative performance indices. Guided by these indices, two performance enhancement approaches can be effectively applied: (1) installing additional sensors which are not included in the piping and instrumentation diagram (P&ID) and (2) executing extra operation steps which are not specified in the sequential function chart (SFC). Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.

Wastewater Minimization of Batch Processes with Single Contaminant

This paper presents a novel method based on mixed integer linear programming (MILP) that addresses the optimization of water network in batch chemical processes with single contaminant to minimize wastewater discharge. In this method, a batch cycle is divided into several time intervals and a unit would be divided into several sub-units, if necessary. It ensures that each unit/sub-unit operates in only one time interval. Each unit/sub-unit is attached with a buffer tank to relax the time constraint. The sequence of units is pre-determined in ascending order of their outlet concentration, so that units and their buffer tanks only provide water to afterward units. A superstructure of units, buffer tanks, and water-using connections is developed. The corresponding mathematical model is a MILP problem that guarantees the global optimum and obtains a solution quickly. Heuristic rules are used to analyze and omit the redundant buffer tanks. The proposed method is applied to a case study in this paper. The results show that the method is effective.

Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation

Making on-specification products is a primary goal, and also a challenge in chemical batch process operation. Due to the uncertainty of raw materials and instability of operating conditions, it may not produce the desired on-spec final product. It would be helpful if one can predict the product quality during each operation, so that one can make adjustments to process conditions in order to make on-spec product. This paper addresses the issue of real-time prediction of final product quality during a batch operation. First, a data-driven modeling approach is presented. This multimodel approach uses available process information up to the current points to capture their time-varying relationships with the final product quality during the course of operation, so that the prognosis of product quality can be obtained in real-time. Then, due to its data-driven nature, the focus is given on how to make the models robust in order to eliminate the effect of noise, especially, outliers in the data. A model-based outlier detection method is presented. The proposed approach is applied to a generic chemical batch case study, with its prediction performance being evaluated.

Fault Diagnosis of Batch Chemical Processes Using a Dynamic Time Warping (DTW)-Based Artificial Immune System

Fault diagnosis is important for ensuring chemical processes stability and safety. The strong nonlinearity and complexity of batch chemical processes make such diagnosis more difficult than that for continuous processes. In this paper, a new fault diagnosis methodology is proposed for batch chemical processes, based on an artificial immune system (AIS) and dynamic time warping (DTW) algorithm. The system generates diverse antibodies using known normal and fault samples and calculates the difference between the test data and the antibodies by the DTW algorithm. If the difference for an antibody is lower than a threshold, then the test data are deemed to be of the same type of this antibody’s fault. Its application to a simulated penicillin fermentation process demonstrates that the proposed AIS can meet the requirements for online dynamic fault diagnosis of batch processes and can diagnose new faults through self-learning. Compared with dynamic locus analysis and artificial neural networks, the proposed me...

Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes

AbstractIn the semiconductor industry, process monitoring has been recognized as a critical component of the manufacturing system. Multivariate statistical process monitoring (SPM) techniques, such as multiway principal component analysis and multiway partial least squares, have been extend to monitor semiconductor processes. These SPM methods require extensive, often off‐line data preprocessing such as data unfolding, trajectory mean shift, and trajectory alignment. This requirement is probably not an issue for the traditional chemical batch processes but it poses a significant challenge for semiconductor batch processes. This is because data preprocessing makes model building and maintenance extremely labor intensive due to the large number of models in a typical semiconductor fab. In addition, semiconductor process data often show more severe nonnormality compared to those of the traditional chemical process under closed‐loop control, which results in suboptimal performance in many applications. To address these challenges, several pattern classification based monitoring (PCM) methods have been developed recently, but some limitations remain and trajectory alignment is still required. In this article, we analyze the fundamental reasons for the limitations of the SPM and PCM methods when applied to monitor semiconductor processes. In addition, we propose a new statistics pattern analysis (SPA) framework to address the challenges associated with semiconductor processes. By monitoring batch statistics, the proposed SPA framework not only eliminates all data preprocessing steps but also provides superior fault detection performance. Finally, we use an industrial example to demonstrate the advantages of the proposed SPA framework, and examine the fundamental reasons for the improved performance from SPA. © 2010 American Institute of Chemical Engineers AIChE J, 2011