Batch fermentation : modeling, monitoring, and control

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The latter would work equally well for the cooking operation but will be more difficult to detect, monitor a thermometer would be needed and regulate. The duration of keeping the spaghetti in hot water will change because of many factors. These include the relative amounts of water and spaghetti the initial charge of ingredients , the tenderness of cooked spaghetti a quality variable that varies with personal taste and weight watching - it is said that absorption of the carbohydrates by the body increases as the spaghetti gets tender , type of spaghetti flour whole wheat or bleached flour , and the amount of heat provided one can turn the heat off and keep the strings in hot water longer.

Consequently, while developing an optimal reference trajectory for this example process, one may have to take into consideration variations in batch run duration and other factors that influence the degree of cooking. Developing a detailed model of this simple process based on first principles may be even more challenging. A simple empirical model based on data may be accurate enough for most needs. Most industrial batch processes have more process and quality variables, and more stringent operational and financial constraints.

Consequently, development of reference trajectories, determination of change point landmark occurrence, quality assessment, and monitoring of process and product safety are much more challenging. This book focuses on batch process modeling, monitoring, fault diagnosis, and control.

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The discovery of a new drug such as a new antibiotic or a new manufacturing method that revolutionizes yield and productivity are critical for commercial success. Biology, chemistry, bioinformatics, and biochemical engineering provide the foundations for these advances. But, large-scale commercial production with consistent product quality, stringent process and product safety requirements, and tight production schedules necessitate a different set of skills built upon systems science, statistics, and control theory.

The focus then shifts to finding optimal reference trajectories and operating conditions, and manufacturing the product profitably in spite of variations in raw materials and ambient conditions, malfunctions in equipment, and variations in operator judgement and experience. Techniques in model development, signal processing, data reconciliation, process monitoring, fault detection and diagnosis, quality control, and process control need to be integrated and implemented.

The book provides a unified source to introduce various techniques in these areas, illustrate many of them, and discuss their advantages and limitations. The book presents both fundamental and data-based empirical modeling methods, several monitoring techniques ranging from simple univariate statistical process control to advanced multivariate monitoring techniques, many fault diagnosis techniques and a variety of simple to advanced process control approaches. Techniques that address critical issues such as landmark detection, data length adjustment, and advanced paradigms that merge monitoring and diagnosis activities by a supervisory knowledge-based system are discussed.

The methods presented can be used in all batch processes by paying attention to the special characteristics of a specific process. The focus of the book is on batch fermentation and pharmaceutical processes. Two reasons APC has previously proven challenging to implement for bioprocesses include: lack of suitable online sensor technology of key system components, and strongly nonlinear first principal models required to predict bioconversion behavior.

To overcome these challenges batch fermentations with the acetogen Moorella thermoacetica were monitored with Raman spectroscopy for the conversion of real lignocellulosic hydrolysates and a kinetic model for the conversion of synthetic sugars was developed. Raman spectroscopy was shown to be effective in monitoring the fermentation of sugarcane bagasse and sugarcane straw hydrolysate, where univariate models predicted acetate concentrations with a root mean square error of prediction RMSEP of 1.

Multivariate partial least squares PLS models were employed to predict acetate, xylose, glucose, and total sugar concentrations for both hydrolysate fermentations. In addition, a screening technique was discussed for improving Raman spectra of hydrolysate samples prior to collecting fermentation data. Furthermore, a mechanistic model was developed to predict batch fermentation of synthetic glucose, xylose, and a mixture of the two sugars to acetate.

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The models accurately described the bioconversion process with an RMSEP of approximately 1 g L-1 for each model and provided insights into how kinetic parameters changed during dual substrate fermentation with diauxic growth. A simple temperature control loop or stirrer speed controller can save a 80, liter batch from getting ruined.

Control of batch fermentation processes can be denned as a sequence of problems. The first problem is the determination of optimal trajectories to be followed during a batch run. Given a good model, this can be cast as an open-loop optimization problem. Another approach for determining these trajectories is to extract them from historical data bases of good batches by using statistical techniques such as principal components analysis. The second problem is the low level closed-loop control of critical process vari- ables. This may be achieved by using several single-input single-output SISO control loops to regulate each controlled variable by manipulating an influential manipulated variable paired with it.

The third problem is higher level control that can be addressed by selecting a multi-loop or a multivariable control approach. The former necessitates the coordination of the operation of SISO loops, the latter focuses on the development of a single controller that regulates all controlled variables by all manipulated inputs.

A modular simulation package for fed-batch fermentation: penicillin production

While such a controller can be built without using any low level SISO loops, practice in other areas has favored the use of SISO loops for redundancy and reliability. In that case, the multivariable controller sup- plies the set-points to SISO loops. The multivariable control system can be based on linear quadratic optimal control theory or model predictive con- trol MFC. The optimal control theory has many success stories in various fields ranging from aerospace to manufacturing and power generation.

In recent years MFC has become appealing because it can handle process con- straints, disturbances, and modeling errors very effectively. MFC involves the solution of a real-time constrained optimization problem at each sam- pling time. While this is a limiting factor, the increase of computation speed and reduction of computation cost over the years works in favor of MFC. Techniques for addressing these three problems are discussed in Chapter 7. Penicillin Fermentation 13 1.

Experienced plant personnel have good insight in integrating var- ious pieces of information provided by process measurements to determine the cause s of a fault. Various fault diagnosis paradigms can automate this effort and provide timely information to plant personnel about the most likely causes for abnormal operation. Reduction of down time, fixing the actual problem as opposed to a secondary fault , and scheduling regular maintenance as opposed to doing emergency repairs contribute significantly over time to the profitability of the process.

The book introduces many alternative fault diagnosis paradigms and illustrates some in more detail through case studies. It also proposes the integration of monitoring and diagnosis activities by linking SPM tools with a real-time knowledge-based system that acts as a supervisor of pro- cess operations and fault diagnosis agent.

Batch Fermentation: Modeling: Monitoring, and Control - CRC Press Book

Fault diagnosis techniques and knowledge-based systems are covered in Chapter 8. A forward-looking proposal for further integration of monitoring and diagnosis with control system performance assessment, supervisory control of batch fermentation process operation, and plantwide decision making systems is presented in Chapter 9.

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Mary's Medical School in London, observed that mould had developed ac- cidentally on a Staphylococcus aureus culture plate that was left on the laboratory bench and that the mould had created a bacteria-free circle around itself. He was inspired to further experiment and he found that a mould culture prevented growth of Staphylococcus, even when diluted times. He named the active substance penicillin []. In December , he and his colleagues Florey and Chain received the Nobel Price in medicine for the discovery of penicillin and its curative effect in various infectious diseases [].

This accidental discovery saved thousands of lives in later years and had a major impact on pharmaceutical production of various antibiotics. Industrial Scale Penicillin Production There are basically two major kinds of antibiotics, namely, narrow- spectrum antibiotics and broad-spectrum antibiotics.

Penicillin cultivation process.

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Penicillin is an example of narrow spectrum antibiotic. Broad-spectrum antibiotics are active against a wide range of microorganisms such as both Gram-negative and Gram-positive bacteria. Tetracycline is an example of a broad-spectrum antibiotic. The penicillin family includes penicillin G, penicillin V, penicillin O, and many synthetic and semisynthetic derivatives such as ampicillin, amox- icillin, nafcillin and ticarcilin. Although penicillin is produced by many Penicillium and Aspergillus strains, industrial penicillin is completely produced by Penicillium chryso- genum.

Highly developed mutants are also used in industry. The medium for penicillin production typically contains an organic nitrogen source e.

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Although it is strain-specific, pH and temperature of cultivation broth are typically between and C, respectively. Under these circumstances, a maximum theoretical yield of penicillin on glucose is estimated to be 0. A typical industrial scale process for penicillin production is shown in Figure 1. Penicillium chrysogenum strains are used to inoculate ml of the medium in a ml flask at 25C. Downstream processes in industrial scale penicillin production. Since formation of sec- ondary metabolites in this case, penicillin is usually not associated with cell growth, it is a common practice to grow the cells in a batch culture followed by a fed-batch operation to promote synthesis of the antibiotic.

Batch Fermentation: Modeling, Monitoring, and Control

The bioreactor is operated for five to six days in fed-batch mode. After the cultivation stage, a series of product recovery techniques are applied depending on the required purity of the final product. Flow diagram for penicillin recovery process is given in Figure 1. A typical time course of penicillin cultivation is represented in Figure 1. First, the cells are grown batchwise until they enter early station- ary phase of batch growth which is also associated with the depletion of substrate.

Then, the process is switched to fed-batch operation that is ac- companied by penicillin production. At this stage, process is said to be in the production phase. Experimental data are displayed in [26]. Detailed discussion of various physiological phases is presented in Section 2. Chapters focus on modeling. Chapter 6 presents a variety of process monitoring techniques. Chapter 7 presents control techniques for batch process operation and Chapter 8 discusses various fault diagnosis paradigms.

Chapter 2 focuses on the development of process models based on first principles. Considering the uncertainty in some reaction and metabolic pathways, and in various parameters, both unstructured and structured kinetic models are discussed. Case studies for penicillin fermentation are presented for both types of models along with simulation results. Chapter 3 presents various concepts and techniques that deal with experimental data collection and pretreatment.