No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A web-based simulator for penicillin fermentation
... The piston simulator is implemented in R, Python, JMP, and Matlab; see [7][8][9]. The PENSIM simulation software is used to model penicillin production in a fed-batch fermentor [10,11]. It is used operationally to monitor and process troubleshooting activities. ...
... The PENSIM simulation models penicillin production [10,11,27,28]. It includes variables such as pH, temperature, aeration rate, agitation power, feed flow rate of the substrate, and a Raman probe. ...
Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance engineering, aiming at adaptable monitoring diagnostic, prognostic, and prescriptive capabilities. A key element introduced here is the role of emulators in this transformation. Emulators, also called surrogate models or metamodels, provide monitoring and diagnostic capabilities. In particular, we focus on an optimisation goal combining optimised and robust performance derived from stochastic emulators. We demonstrate the methodology using two open-source examples and show how emulators can be used to complement finite element and computational fluid dynamic models in digital twin frameworks. The case studies consist of a mechanical system and a biological production process.
... To demonstrate BOL we use PENSIM, a biotechnology simulator described in Kenett (2024). The PENSIM simulation software is modelling penicillin production (Birol et al., 2001, PENSIM v2, 2023) in a fed-batch penicillin production process and includes variables such as pH, temperature, aeration rate, agitation power, feed flow rate of the substrate and a Raman probe. The response we use is P, the final penicillin concentration measured in grams per litter. ...
... In this setting, a common quick fix that has been adopted so far is to throw overboard some data under the assumption that processes are oversampled and nothing meaningful would be lost by discarding a part of it. Sub-sample and multirate schemes fall in this category [5][6][7][8][9][10][11][12][13][14][15][16]. However, an alternative approach is gaining importance: data aggregation. ...
As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.
... The fed-batch penicillin fermentation is a well-known benchmark process which has obvious nonlinear dynamics and multiphase characteristics (Birol et al., 2001). In this section, the fed-batch penicillin fermentation process is utilized to demonstrate the effectiveness of the proposed method. ...
Batch and semi-batch processes are often characterized with strong nonlinearity. Most of the existing kernel-based methods proposed for nonlinear batch process monitoring have not considered the nonlinear characteristic, sequential phase division and uneven problem simultaneously. In this article, a novel similarity index is first defined on the basis of the kernel technique to describe the similarity of nonlinear characteristic in the high feature space. Then, the pseudo time-slice is constructed for each sample by searching samples within a range resembling to each time using the k-nearest neighbor (kNN) rule, which can effectively tackle the uneven problem without trajectory synchronization. A novel automatic sequential phase division procedure is proposed by analysing the nonlinear similarity between the local models derived from the pseudo time-slice and the representative model of each phase. For online application, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to fed-batch penicillin fermentation process.
... Another example is PENSIM (Pensim -a Web Based Program for Dynamic Simulation of Fed-Batch Penicillin Production, Birol et al. 2001Birol et al. , 2002, a web-based simulator that runs in all computer platforms. It simulates the fed-batch penicillin production process, based on an unstructured model. ...
Modern technology is affecting what is taught and how statistics is taught. Computer intensive methods like bootstrapping, generalized regression, coordinate exchange algorithms and computer experiment emulators are now part of the statistical toolkit. In parallel with these new methods and tools, a similar evolution appears in statistical education. Simulators offer opportunities for hands-on experience in the classroom, bridging the gap between theory and practice. In this paper we map a wide range of simulation based tools used in teaching statistical methods, putting them in the context of a quality ladder representing various maturity levels of organisations and course level sophistication. The mapping is designed to help educators assess and integrate options related to the incorporation of simulators as teaching aids in an educational and pedagogical roadmap. The mapping is applied to specific simulation products but provides a general framework that can be expanded and used in a generic way.
... In this section, the MGMM based adaptive kernel PLS method is compared to the conventional kernel PLS model on a simulated fed-batch penicillin fermentation process [21] and the accuracy of the soft sensor predictions is evaluated. The fermentation process is used to produce antibiotic that is the secondary metabolite of microbial cell culture. ...
Soft sensor technique has become increasingly important to provide reliable on-line measurements, facilitate advanced process control and improve product quality in process industries. The conventional soft sensors are normally single-model based and thus may not be appropriate for processes with shifting operating conditions or phases. In this study, a multiway Gaussian mixture model (MGMM) based kernel partial least squares (PLS) method is proposed to handle multiple operating phases in batch or semibatch processes. The measurement data are first projected onto high-dimensional kernel feature space to account for the process nonlinearity. Then the multiway Gaussian mixture model is estimated with multiple Gaussian clusters in the kernel space. Thus various localized PLS models can be built within each Gaussian cluster to characterize the dynamics in the particular operating phase. Using Bayesian inference strategy, the soft sensor models for all the test samples are adaptively selected from the multiple localized kernel PLS models representing different phases and further used for online quality predictions. The presented soft sensor method is applied to the multiphase penicillin fermentation process and the computational results demonstrate that its performance is superior to the conventional multiway kernel PLS model.
... In this case study, the data sets for calibration and validation were obtained using the Pensim dynamic simulation of fed-batch penicillin production , described in [21]. There is a total of 6 * 40 = 240 columns in X that contain the trajectories of the MV during the evolution of the batch, the initial conditions are not added as columns in this example as they are equal for all of the batches. ...
A novel numerical optimization PLS-based algorithm to enable the identification of two-block two-way linear models is proposed. The approach is denoted Expanded PLS (E-PLS) and takes advantage of the simplicity of use of two-way models for batch processes and tackles its major limitation which is over-fitting. Over-fitting is caused when the variance of the error term dominates the prediction error of the model. The variance error term dominates when dealing with short and fat data sets, which is typical in batch processes. E-PLS tackles over-fitting by minimizing a prediction error cost function with an expanded number of error terms. The expansion trades bias for variance of the error yielding a better model in terms of predictive performance. Two fed-batch fermentation examples show how E-PLS can outperform the traditional PLS algorithm in terms of predictive performance of the obtained model. The improved predictive performance of E-PLS does come at the expense of increased computational cost. However, this is not normally a problem since most models will be calibrated off-line. Although E-PLS has been designed with batch processes in mind, it could potentially serve to obtain better models for calibration problems with short and fat data sets.
Topik tentang kompos, pupuk organik cair dan biogas memiliki peranan yang penting di masa depan, terkait dengan pertanian yang ramah lingkungan. Selain itu mempunyai peluang yang bisa dikembangkan untuk memenuhi kebutuhan manusia masa kini dan masa mendatang. Alasan lainnya adalah aktivitas kehidupan sehari-hari di berbagai tempat, seperti di pasar, rumah tangga, industri pengolahan hasil pertanian, peternakan, perkebunan, perikanan, kehutanan, pertanian tanaman pangan dan hortikultura, yang banyak menghasilkan limbah organik. Keberadaan limbah semacam ini sangat mengganggu kenyamanan lingkungan hidup dan mejandi beban yang menghabiskan banyak dana untuk menanganinya. Oleh karena itu, perlu diupayakan mencari alternative penanganan sehingga memberikan kemanfaatan yang sangat luas dan bernilai ekonomis.
Buku ini dapat digunakan oleh banyak kalangan baik di kampus terutama di fakultas pertanian, peternakan, teknologi pertanian khusus prodi teknik pertanian dan biosistem, juga bagi para masyarakat umum seperti petani. Beberapa materi dan praktik yang disajikan di dalam buku mudah difahami oleh pembaca yang tidak pernah mendapatkan kuliah khusus sekalipun tentang tema-tema di dalam buku ini. Materi-materi tersebut antara lain, peranan pupuk organik pada pertanian, limbah peternakan (sapi dan ayam), limbah pertanian, proses pengomposan, reaksi kimia pada proses pengomposan, metode pengomposan, dan kualitas kompos (standar mutu dan penerapannya dalam pertanian), pupuk organic cair, teknologi bioproses pupuk organic cair, biogas dan biodigester.
Topik tentang teknologi bioproses memiliki peranan yang penting di masa depan; semisal dalam bidang pangan, farmasi (obat-obatan), energi, kedokteran, pertanian, maupun pengolahan limbah berbahaya (bioremediasi). Peran teknologi bioproses dalam bidang-bidang tersebut merupakan peluang yang bisa dikembangkan untuk memenuhi kebutuhan manusia kini dan di masa mendatang.
Lebih dari itu, alasan lainnya antara lain karena aktivitas kehidupan sehari-hari di berbagai tempat, seperti di pasar, rumah tangga, industri pengolahan hasil pertanian, peternakan, perkebunan, perikanan, kehutanan, pertanian tanaman pangan, dan hortikultura, terdapat banyak sekali limbah khususnya limbah organik; limbah yang berbentuk padat kemudian diistilahkan dengan sampah. Keberadaan limbah semacam ini dirasa cukup mengganggu kenyamanan lingkungan hidup dan lebih jauh merupakan beban yang menghabiskan dana relatif besar untuk menanganinya. Oleh karena itu, perlu ditangani dengan sedemikian rupa sehingga dapat memberikan kemanfaatan dan bernilai ekonomis.
Buku ini dapat digunakan oleh banyak kalangan baik di kampus terutama di fakultas pertanian maupun fakultas peternakan, juga bagi para masyarakat umum seperti petani. Beberapa materi dan praktik yang disajikan dalam buku ini mudah dipahami oleh pembaca yang tidak pernah mendapatkan kuliah khusus sekalipun tentang tema-tema peranan pupuk organik pada pertanian, limbah peternakan (sapi dan ayam), limbah pertanian, proses pengomposan, reaksi kimia pada proses pengomposan, metode pengomposan, dan kualitas kompos (standar mutu dan penerapannya dalam pertanian).
This paper deals with fault detection and isolation (FDI) in a fed-batch penicillin fermentation process. The method is based on an empirical model developed using the multi-way partial least squares technique. The paper begins by demonstrating how this model can be used to provide FDI capabilities and then shows how this FDI scheme can be integrated within a model predictive controller to provide accurate control in the presence of fault conditions within the process.
A new multiway discrete hidden Markov model (MDHMM)-based approach is proposed in this article for fault detection and classification in complex batch or semibatch process with inherent dynamics and system uncertainty. The probabilistic inference along the state transitions in MDHMM can effectively extract the dynamic and stochastic patterns in the process operation. Furthermore, the used multiway analysis is able to transform the three-dimensional (3-D) data matrices into 2-D measurement-state data sets for hidden Markov model estimation and state path optimization. The proposed MDHMM approach is applied to fed-batch penicillin fermentation process and compared to the conventional multiway principal component analysis (MPCA) and multiway dynamic principal component analysis (MDPCA) methods in three faulty scenarios. The monitoring results demonstrate that the MDHMM approach is superior to both the MPCA and MDPCA methods in terms of fault detection and false alarm rates. In addition, the supervised MDHMM approach is able to classify different types of process faults with high fidelity. © 2011 American Institute of Chemical Engineers AIChE J, 2012
The predictive model based soft sensor technique has become increasingly important to provide reliable online measurements, facilitate advanced process control and optimization, and improve product quality in process industries. The conventional soft sensors are normally single-model based and thus may not be appropriate for processes with shifting operating phases or conditions and the underlying changing dynamics. In this study, a multiway Gaussian mixture model (MGMM) based adaptive kernel partial least-squares (AKPLS) method is proposed to handle online quality prediction of batch or semibatch processes with multiple operating phases. The three-dimensional measurement data are first preprocessed and unfolded into two-dimensional matrix. Then, the multiway Gaussian mixture model is estimated in order to identify and isolate different operating phases. Further, the process and quality measurements are separated into multiple segments corresponding to those identified phases, and the various localized kernel PLS models are built in the high-dimensional nonlinear feature space to characterize the shifting dynamics across different operating phases. Using Bayesian inference strategy, each process measurement sample of a new batch is classified into a particular phase with the maximal posterior probability, and thus, the local kernel PLS model representing the identical phase can be adaptively chosen for online quality variable prediction. The presented soft sensor modeling method is applied to a simulated multiphase penicillin fermentation process, and the computational results demonstrate that the proposed MGMM-AKPLS approach is superior to the conventional kernel PLS model in terms of prediction accuracy and model reliability.
Multiway kernel partial least squares method (MKPLS) has recently been developed for monitoring the operational performance of nonlinear batch or semi-batch processes. It has strong capability to handle batch trajectories and nonlinear process dynamics, which cannot be effectively dealt with by traditional multiway partial least squares (MPLS) technique. However, MKPLS method may not be effective in capturing significant non-Gaussian features of batch processes because only the second-order statistics instead of higher-order statistics are taken into account in the underlying model. On the other hand, multiway kernel independent component analysis (MKICA) has been proposed for nonlinear batch process monitoring and fault detection. Different from MKPLS, MKICA can extract not only nonlinear but also non-Gaussian features through maximizing the higher-order statistic of negentropy instead of second-order statistic of covariance within the high-dimensional kernel space. Nevertheless, MKICA based process monitoring approaches may not be well suited in many batch processes because only process measurement variables are utilized while quality variables are not considered in the multivariate models. In this paper, a novel multiway kernel based quality relevant non-Gaussian latent subspace projection (MKQNGLSP) approach is proposed in order to monitor the operational performance of batch processes with nonlinear and non-Gaussian dynamics by combining measurement and quality variables. First, both process measurement and quality variables are projected onto high-dimensional nonlinear kernel feature spaces, respectively. Then, the multidimensional latent directions within kernel feature subspaces corresponding to measurement and quality variables are concurrently searched for so that the maximized mutual information between the measurement and quality spaces is obtained. The I2 and SPE monitoring indices within the extracted latent subspaces are further defined to capture batch process faults resulting in abnormal product quality. The proposed MKQNGLSP method is applied to a fed-batch penicillin fermentation process and the operational performance monitoring results demonstrate the superiority of the developed method as apposed to the MKPLS based process monitoring approach.
Inherent process and measurement uncertainty has posed a challenging issue on soft sensor development of batch bioprocesses. In this paper, a new soft sensor modeling framework is proposed by integrating Bayesian inference strategy with two-stage support vector regression (SVR) method. The Bayesian inference procedure is first designed to identify measurement biases and misalignments via posterior probabilities. Then the biased input measurements are calibrated through Bayesian estimation and the first-stage SVR model is thus built for output measurement reconciliation. The inferentially calibrated input and output data can be further used to construct the second-stage SVR model, which serves as the main model of soft sensor to predict new output measurements. The Bayesian inference based two-stage support vector regression (BI-SVR) approach is applied to a fed-batch penicillin cultivation process and the obtained soft sensor performance is compared to that of the conventional SVR method. The results from two test cases with different levels of measurement uncertainty show significant improvement of the BI-SVR approach over the regular SVR method in predicting various output measurements.
Biotechnology is one of the active domains in the NSF funded Engineering Research Center VaNTH (Vanderbilt, Northwestern, University of Texas, and Harvard/MIT) where educational modules have been developed. These modules cover a collection of challenges designed around bioreactors, mass and momentum transfer issues, microbial kinetics, and downstream processing, which are among core biotechnology topics. The aim of this study was to design educational modules centered on challenge-based education and to implement them in classroom settings. This paper focuses on the design and implementation of such educational modules and provides an overview of the challenges and learning activities that were developed for three specific topics that have been implemented at Northwestern and Vanderbilt Universities.
A novel batch bioprocess monitoring approach based on multiway kernel localized Fisher discriminant analysis (MKLFDA) is proposed in this article. For the routine bioprocess operation with some abnormal events, a supervised monitoring method is needed to handle the training data set that includes various types of faulty samples. Because of the inherent process nonlinearity and multimodality among normal and faulty clusters, the traditional multiway Fisher discriminant analysis (MFDA) method becomes inappropriate and unable to effectively detect or classify faulty samples. The newly developed MKLFDA approach, however, combines kernel function with the localized Fisher discriminant analysis so that the “kernel” feature can retain the process nonlinearity while the “localized” characteristic is able to extract the multi-Gaussianity within data clusters. Furthermore, the integrated multiway analysis uses batch-wise unfolding to convert the three-dimensional data set into a two-dimensional matrix that can be fed into the kernel localized Fisher discriminant analysis for fault detection and classification. The proposed MKLFDA approach is applied to three test scenarios in the fed-batch penicillin fermentation process and its batch monitoring performance is compared to that of the conventional MFDA method. The results indicate that the MKLFDA approach performs much better than MFDA method in detecting abnormal operating conditions as well as classifying various types of process faults occurring in fed-batch operation. The MKLFDA approach results in higher fault detection rate and lower false classifications.
Industrial fermentations typically use media that are balanced with multiple substitutable substrates including complex carbon and nitrogen source. Yet, much of the modeling effort to date has mainly focused on defined media. Here, we present a structured model that accounts for growth and product formation kinetics of rifamycin B fermentation in a multi-substrate complex medium. The phenomenological model considers the organism to be an optimal strategist with an in-built mechanism that regulates the sequential and simultaneous uptake of the substrate combinations. This regulatory process is modeled by assuming that the uptake of a substrate depends on the level of a key enzyme or a set of enzymes, which may be inducible. Further, the fraction of flux through a given metabolic branch is estimated using a simple multi-variable constrained optimization. The model has the typical form of Monod equation with terms incorporating multiple limiting substrates and substrate inhibition. Several batch runs were set up with varying initial substrate concentrations to estimate the kinetic parameters for the rifamycin overproducer strain Amycolatopsis mediterranei S699. Glucose and ammonium sulfate (AMS) demonstrated significant substrate inhibition toward growth as well as product formation. The model correctly predicts the experimentally observed regulated simultaneous uptake of the substitutable substrate combinations under different fermentation conditions. The modeling results may have applications in the optimization and control of rifamycin B fermentation while the modeling strategy presented here would be applicable to other industrially important fermentations.
ResearchGate has not been able to resolve any references for this publication.