Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process

The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., “If A then do B”. As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.     

An adaptive control algorithm for fed-batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.     

A Cascading Auction Protocol as a Framework for Integrating Process Planning and Heterarchical Shop Floor Control

A new contract net-style auction protocol is proposed as a framework for integrating process planning and shop floor control in heterarchical manufacturing systems. Process planning is partitioned into on-line and off-line activities; off-line process planning decisions are represented in a graph format and used as input for on-line process planning activities performed by machine controllers. Triggered by the opening round of an auction, the final on-line stages of process planning are dovetailed with the resource allocation process in the shop floor control system. The auction process allows final process planning decisions to be based on timely information, relying on the distribution of static process planning information rather than the distribution of a model of dynamic shop floor status and allowing a controller to identify all the primary and secondary resources and operations that must be provided for the incremental processing of a part.     

Process analytical technology implementation for protein refolding: GCSF as a case study

Process analytical technology is gaining interest in the biopharmaceutical industry as a means to enable consistency in processing and thereby in product quality via process control. Protein refolding is known to be significantly impacted by critical process parameters and feed material attributes including composition and pH of the solubilisation and refolding buffers. Hence, to achieve robust process control and product quality, these attributes and parameters need to be monitored. This paper presents an approach towards statistical process control and monitoring of protein refolding, from buffer preparation to refold quenching, during manufacturing of therapeutic proteins from Escherichia coli based systems. The proposed approach utilises measurements of online redox potential, temperature, and pH for development of a statistical model. The model has then been integrated with LabView to permit real-time monitoring of the refolding process. The proposed system has been demonstrated to successfully identify process deviations and thereby enable process control for manufacturing product of consistent quality.     

Turgor-regulated translocation

Abstract. The role played by potassium in plants is examined, in particular its connection with phloem translocation. The possibility emerges that trans-location may be a turgor-regulated process in which potassium plays a central role.
A simple mechanistic hypothesis is proposed and the evidence for it is discussed. The hypothesis is strongly supported by both the direct and the circumstantial evidence.
An important conclusion is the need to reassess the importance of the translocation process in the control of assimilate partitioning. Hitherto, as implied by Münch's hypothesis, control has been assumed to be exerted by source and/or sink activity with the translocation pathway playing a more passive secondary role. If the present proposal is correct the translocation process emerges instead as a dominant factor in the control of assimilate partitioning.     

Optimal feeding strategies by adaptive mesh selection for fed-batch bioprocesses

The objective of this contribution is the design of optimal feeding strategies for fed-batch bioprocesses, where complex dynamic models with input and state constraints are present. For the solution of this dynamic optimization problem a transformation to a finite dimensional optimization problem is made using piecewise linear control profiles. The optimization of these profiles is performed by a sequential approach, that includes an ODE solver for the solution of the model ODE's. Further an adaptive mesh selection algorithm was investigated for an appropriate discretization of the control profiles. The implementation of the resulting optimal feeding profiles is shown for a process example, namely the production of nikkomycin by Streptomyces tendae. This implementation uses a hierarchical process control framework, that consists of components for process monitoring, state estimation, and trajectory control.     

Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production

The fed-batch process for commercial production of riboflavin (vitamin B2) was optimized on-line using model-predictive control based on artificial neural networks (ANNs). The information required for process models was extracted from both historical data and heuristic rules. After each cultivation the process model was readapted off-line to include the most recent process data. The control signal (feed rate), however, was optimized on-line at each sampling interval. An optimizer simulated variations in the control signal and assessed the forecasted model outputs according to an objective function. The optimum feed profile for increasing the product yield (YB2/S) and the amount of riboflavin at the time of harvesting was adjusted continuously and applied to the process. In contrast to the control by set-point profiles, the novel ANN-control is able to react on-line to variations in the process and also to incorporate the new process information continuously. As a result, both the total amount of riboflavin produced and the product yield increased systematically by more than 10% and the reproducibility of seven subsequently optimized batches was enhanced.     

Multilayer adaptive control of continuous bioprocesses using optimising control technique. Case study: Bakers' yeast culture

High costs associated with many fermentation processes in an increasingly competitive industry make any prompt application of modern control techniques to industrial bioprocesses very desirable. However, this is often hampered by the lack of adequate mathematical models, on the one hand, and by the absence of continuous, on-line measurement of the most relevant process variables, on the other hand. This paper addresses these problems and offers a new strategy to control continuous bioprocesses using a hierarchical structure such that neither structured process models nor continuous measurement of all relevant variables have to be available. The control system consists of two layers. The lower layer represents a dynamic adaptive follow-up control of a continuously measured output — in our case dissolved oxygen concentration. This variable is supposed to be strongly correlated with the key output variable — in our case cellular concentration which is not continuously available for measurement. The higher layer is then designed to maintain a desired profile of the process key output using a set-point optimising control technique. The Integrated System Optimisation and Parameter Estimation method used operates on an appropriately chosen steady-state performance criterion. A prerequisite for successful application of the proposed approach is an approximate steady-state model, describing the relationship between the measured output and the process key output variable. Furthermore, occasional in situ, off-line or laboratory measurement values of the key output variable are needed. Promising simulation results of the biomass concentration control, by manipulating the air flow-rate in the continuous bakers' yeast culture are presented.     

城市生态观测:质量控制对象、体系和规范编制

城市生态系统长期观测是开展城市生态安全评价和制定人居环境健康管理政策的基础。为了确保城市生态观测数据的准确性和科学性,通过辨识城市生态观测质量控制的概念,基于"人机料法环"理论构建了城市生态观测质量控制体系,结合城市生态观测质量控制相关规范的编制与实施现状,探讨了城市生态观测质量控制规范的编制技术。结果表明：（1）城市生态观测质量控制是面向城市生态观测内容（要素、空间格局、功能、服务）,为保证生态观测全过程质量要求、提高生态观测结果的准确性而实施的质量控制活动和措施。（2）城市生态观测质量控制体系可从对象和过程这两个维度进行界定：对象维度方面,包括"人"-人员、"机"-仪器设备、"料"-数据、"法"-文件和"环"-观测过程5个方面;过程维度方面,涵盖城市生态观测全过程,包括城市生态观测前期准备工作、观测过程中以及观测后的数据录入、审核与评价等环节的质量控制措施。（3）我国已发布的生态观测相关技术标准中,发布的时间越晚,质量控制内容越完整,目前生态观测相关标准共102则,仅38则明确提出了质量控制具体要求。（4）《城市生态观测质量控制规范》编制需包含质量体系和质量控制措施两项技术内容,主要包括前引、正文和附录三个章节,对生态观测全过程质量保证和质量控制技术进行规定。     

Twenty-four well plate miniature bioreactor system as a scale-down model for cell culture process development

Increasing the throughput and efficiency of cell culture process development has become increasingly important to rapidly screen and optimize cell culture media and process parameters. This study describes the application of a miniaturized bioreactor system as a scaled-down model for cell culture process development using a CHO cell line expressing a recombinant protein. The microbioreactor system (M24) provides non-invasive online monitoring and control capability for process parameters such as pH, dissolved oxygen (DO), and temperature at the individual well level. A systematic evaluation of the M24 for cell culture process applications was successfully completed. Several challenges were initially identified. These included uneven gas distribution in the wells due to system design and lot to lot variability, foaming issues caused by sparging required for active DO control, and pH control limitation under conditions of minimal dissolved CO2. A high degree of variability was found which was addressed by changes in the system design. The foaming issue was resolved by addition of anti-foam, reduction of sparge rate, and elimination of DO control. The pH control limitation was overcome by a single manual liquid base addition. Intra-well reproducibility, as indicated by measurements of process parameters, cell growth, metabolite profiles, protein titer, protein quality, and scale-equivalency between the M24 and 2 L bioreactor cultures were very good. This evaluation has shown feasibility of utilizing the M24 as a scale-down tool for cell culture application development under industrially relevant process conditions.     

Implementing Process Analytical Technology for the Production of Recombinant Proteins in Escherichia coli Using an Advanced Controller Scheme

The performance of a bioreactor in meeting process goals is affected by the microorganism used, medium composition, and operating conditions. A typical bioreactor uses a supervisory control and data acquisition (SCADA) system for control, and a combination of software and hardware tools for real‐time data analysis. However, when the process is disrupted by utility or instrumentation failure, typical process controllers may be unable to reinstate normal operating conditions before the cells in the reactor shift to unfavorable metabolic regimes. The objective of this study is to examine how the response of a controller affects process recovery when disruptive incidences occur under a process analytical technology (PAT) framework. The process used for this investigation is the production of lethal toxin‐neutralizing factor (LTNF) by Escherichia coli (E. coli), which is controlled by a decoupled input–output‐linearizing controller (DIOLC). The performance of the DIOLC is compared to a proportional integral derivative (PID) controller subjected to the same conditions. The disruptions are introduced manually and the effect of controller action on process recovery and LTNF synthesis is measured in terms of peak purity and concentration. It is observed that DIOLC performs better after reinstating operating conditions and results in a meaningful improvement in performance.     

Use of near-infrared spectroscopy (NIRs) in the biopharmaceutical industry for real-time determination of critical process parameters and integration of advanced feedback control strategies using MIDUS control

Control of biopharmaceutical processes is critical to achieve consistent product quality. The most challenging unit operation to control is cell growth in bioreactors due to the exquisitely sensitive and complex nature of the cells that are converting raw materials into new cells and products. Current monitoring capabilities are increasing, however, the main challenge is now becoming the ability to use the data generated in an effective manner. There are a number of contributors to this challenge including integration of different monitoring systems as well as the functionality to perform data analytics in real-time to generate process knowledge and understanding. In addition, there is a lack of ability to easily generate strategies and close the loop to feedback into the process for advanced process control (APC). The current research aims to demonstrate the use of advanced monitoring tools along with data analytics to generate process understanding in an Escherichia coli fermentation process. NIR spectroscopy was used to measure glucose and critical amino acids in real-time to help in determining the root cause of failures associated with different lots of yeast extract. First, scale-down of the process was required to execute a simple design of experiment, followed by scale-up to build NIR models as well as soft sensors for advanced process control. In addition, the research demonstrates the potential for a novel platform technology that enables manufacturers to consistently achieve “goldenbatch” performance through monitoring, integration, data analytics, understanding, strategy design and control (MIDUS control). MIDUS control was employed to increase batch-to-batch consistency in final product titers, decrease the coefficient of variability from 8.49 to 1.16%, predict possible exhaust filter failures and close the loop to prevent their occurrence and avoid lost batches.     

Bioreactor performance: a more scientific approach for practice

In practice, the performance of a biochemical conversion process, i.e. the bioreactor performance, is essentially determined by the benefit/cost ratio. The benefit is generally defined in terms of the amount of the desired product produced and its market price. Cost reduction is the major objective in biochemical engineering. There are two essential engineering approaches to minimizing the cost of creating a particular product in an existing plant. One is to find a control path or operational procedure that optimally uses the dynamics of the process and copes with the many constraints restricting production. The other is to remove or lower the constraints by constructive improvements of the equipment and/or the microorganisms. This paper focuses on the first approach, dealing with optimization of the operational procedure and the measures by which one can ensure that the process adheres to the predetermined path. In practice, feedforward control is the predominant control mode applied. However, as it is frequently inadequate for optimal performance, feedback control may also be employed. Relevant aspects of such performance optimization are discussed.     

Mass spectrometric evaluation of upstream and downstream process influences on host cell protein patterns in biopharmaceutical products

For production of different monoclonal antibodies (mAbs), biopharmaceutical companies often use related upstream and downstream manufacturing processes. Such platforms are typically characterized regarding influence of upstream and downstream process (DSP) parameters on critical quality attributes (CQAs). CQAs must be monitored strictly by an adequate control strategy. One such process-related CQA is the content of host cell protein (HCP) which is typically analyzed by immunoassay methods (e.g., HCP-ELISA). The capacity of the immunoassay to detect a broad range of HCPs, relevant for the individual mAb-production process should be proven by orthogonal proteomic methods such as 2D gel electrophoresis or mass spectrometry (MS). In particular MS has become a valuable tool to identify and quantify HCP in complex mixtures. We evaluate up- and DSP parameters of four different biopharmaceutical products, two different process variants, and one mock fermentation on the HCP pattern by shotgun MS analysis and ELISA. We obtained a similar HCP pattern in different cell culture fluid harvests compared to the starting material from the downstream process. During the downstream purification process of the mAbs, the HCP level and the number of HCP species significantly decreased, accompanied by an increase in diversity of the residual HCP pattern. Based on this knowledge, we suggest a control strategy that combines multi product ELISA for in-process control and release analytics, and MS testing for orthogonal HCP characterization, to attain knowledge on the HCP level, clusters and species. This combination supports a control strategy for HCPs addressing safety and efficacy of biopharmaceutical products.     

Applying neural networks as software sensors for enzyme engineering

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.     

On a Continuous Branching Process and Birth-Death and Immigration Models for Testing the Efficacy of Some Control Programmes for AIDS

The present communication is an attempt to describe the mode of propagation of AIDS epidemic and its control programme using a branching process as well as a birth-death and immigration model. A comparison of the project of AIDS control programme on the basis of its propagation by a continuous branching process model with that of a linear birth and death process with immigration shows a remarkable contrast. Branching process model shows that it is possible to control the propagation of the disease by suitably increasing the detection rate and lowering the infection rate. However, the propagation of AIDS models by birth and death Process with or without immigration shows that it is increasingly difficult to control the invasion of AIDS merely by controlling the birth, death and immigration parameters.     

Monitoring and control of the physiological state of cell cultures

Advances in bioprocess engineering depends ultimately on the level of understanding and control of the physiological state of the cell population. Process efficiency is strongly influenced by changes in the cellular state which should be monitored, interpreted, and, if possible, properly manipulated. In most control systems this function is not explicitly considered, which hampers process development and optimization. Conventional control logic is based on direct mapping of the growth environment into process efficiency, thereby bypassing the cell state as an intermediate control objective. Today, this limitation is well realized, and explicit monitoring and control of cellular physiology are considered to be among the most challenging tasks of modern bioprocess engineering. We present here a generic methodology for the design of systems capable of performing these advanced monitoring and control functions.The term "physiological state" is quantified by a vector composed of several process variables that convey significant information about cellular state. These variables can be selected among different classes, including specific metabolic rates, metabolic rate ratios, degees of limitation, and others. The real-time monitoring of many of these is possible using commercial sensors. The definition and calculation of representative sets of physiological state variables is demonstrated with examples from several fermentor cultures: recombinant Escherichia coli for phenylalanine production, bioluminescent E. coli (harboring lux genes driven by a heat shock protein promoter) for detection of environmental pollutants, plant cell culture of Perilla frutescensfor anthocyanin production, and perfusion cultures of recombinant mammalian cells (NS0 and CHO) for therapeutic protein production.If the physiological state vector is on-line calculated, the fermentation process can be described by its trajectory in a space defined by the vector components. Then, the goal of the control system is to maintain the physiological state of the cell as close as possible to the trajectory, providing maximum efficiency. A control structure meant to perform this function is proposed, along with the mechanism for its design. In contrast to conventional systems which work in a closed loop in respect to the cell environment, this scheme operates in a closed loop in respect to the cell state. The discussed control concept has been successfully applied to the recombinant phenylalanine production, resulting in physiologically consistent operation, total computer control, and high process efficiency. Initial results from the application of the method to perfusion mammalian cell cultures are also presented. (c) 1996 John Wiley & Sons, Inc.