Product and Process Modelling: A Case Study Approach
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The model was then extended to study the feasibility of making the same product in a series of CSTRs. In many cases the licensor has no motivation to undertake a detailed study as it has limited economic impact on their design. There are many advantages to using dynamic modelling for distillation pressure relief valve sizing. This presentation illustrates such an approach on an ethylene fractionator, which has a significant flare load even after mitigation.
The study also identified potential for additional mitigation. Dynamic simulation was used to predict the unit behaviour, in particular the preferred sequence of operations and expected reaction times of the system to process variations, in order to facilitate the start-up, shut-down and normal operating scenarios. Peter Drogt describes the general benefits resulting from the work, as well as the modelling process, which involved multiple disciplines within DSM, and communication to the local plant.
It is continually investing in new technologies to improve its efficiency and make its plants safer and more profitable.http://argo-karaganda.kz/scripts/wiwybep/2527.php
Business process modeling
Yasser describes how, through an overall plant optimiser, the set-points for reaction conversions in the furnaces are set, and how the model is embedded into the advanced process control system. The model itself works as a soft sensor, providing information on difficult or impossible-to-read plant information such as tube metal temperatures. This leads to safer and more profitable operation. He illustrates how real-time plant data is utilised to perform short-and long-term monitoring of furnace operation, furnace run length prediction, real-time optimisation of the furnace section and many other activities.
Applying Business Process Modeling Techniques: Case Study | Open Access Journals
Keynote 2: Using system modelling to accelerate drug substance development Neil Hodnett, GSK Systems modelling approaches have the potential to accelerate drug substance development, for example by reducing the number of experiments required to deliver a design space and minimizing the risk of re-work. In addition, system models can be used to facilitate the identification of critical process parameters in technical risk assessments. This presentation will illustrate the benefits systems modelling has delivered in GSK using two continuous manufacturing case studies and outline our intent to embed this approach earlier in drug substance development.
In particular, continuing enhancements to high-performance computing HPC , parallelisation, and execution speed of the underlying solvers are aimed at ensuring that high-fidelity models can be solved rapidly and robustly online or in computationally intensive calculations such as Global System Analysis.
New External Model Validation and multi-start Model Validation capabilities help to streamline the combination of first-principles models with experimental, pilot or plant data for added predictive capability. There are also major usability improvements at all levels. The latter represents a state-of-the-art group contribution approach which offers the possibility of predicting the properties of pure components and mixtures with little or sometimes, no reliance on experimental data.
Leader of the gSAFT development Tom Lafitte will present an overview of gSAFT and its capabilities, especially in terms of predicting properties of complex fluids that pose serious challenges to other methods. Particular emphasis will be placed on recent developments that allow the simultaneous handling of phase and reaction equilibrium calculations, including solid phases of organic and inorganic compounds. From experimental data to validated model — an integrated workflow David Slade, PSE gPROMS has always provided advanced capabilities for calibrating models by estimating model parameters from steady-state and dynamic experimental or plant data.
These have been significantly augmented by recent developments that now provide comprehensive capabilities for data import and processing, the ability to specify which experiments are to be used for parameter estimations and which for validating the resulting fit, and automated checking of the fitted parameters. David Slade, one of the architects of the new capabilities, demonstrates how they provide a streamlined, integrated workflow for building the high-fidelity predictive models required for digital design.
Models can be delivered in a simple, easily configurable format that masks the complexity of the underlying models for users, while taking advantage of the execution power of high-performance computing HPC and cloud computing facilities where necessary. This work's aim is to evaluate the minimum data requirement to obtain a suitable model that could be used to support process development and transfer. It was found that with a reasonable amount of experiments, a satisfactory model could be built providing additional insight into the mechanisms of crystallization of an API.
On-line measurements of solution concentration and off-line crystal size density measurements are used to identify the model parameters at lab scale. Parameter fitting was implemented sequentially, firstly to data where nucleation occurred, then to data with dissolution and growth and finally the entire data set with a breakage model. Model discrimination was also utilized for the nucleation, dissolution and breakage models.
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Model validation was attempted with the lab scale model using pilot-plant scale data, followed by model refinement with the pilot-plant data. The refined model obtained from the fitting to lab and pilot-plant data was used to predict crystallization and breakage at commercial scale with various reactor and milling processing conditions. The model enabled appropriate equipment sourcing and operating decisions.
In order to achieve this, the sector is making better use out of cyber-physical production systems with a specific focus on digital twins and how they can be used to hugely minimise the amount of material utilised in the design of a manufacturing process. However, these models which will be used to build a digital twin are not focused on a single piece of equipment, process or even physics, but are holistic models which aggregate data from multiple sources.
Interfacing with all this data in a manageable manner is challenging, especially for a non-modeller. A large number of process parameters such as cooling and agitation rate, seed and solid loading, starting operating temperature and supersaturation play a major role on the outcome of the crystallization process. However, batch processes have the disadvantages of variability between batch productions, high costs, long production time and scale-up difficulties.
The implementation of continuous crystallization processes to deliver high quality medicines show the need for modelling and simulation to optimize the obtained particles, with acceptable experimental effort and time. The implemented model in gPROMS FormulatedProducts represents a cascade of three MSMPR crystallizers with intermittent withdrawal and a rotor stator wet mill integrated in the first stage used for nucleation induction and milling. The presented procedure shows the different steps of parameter estimation for the various crystallization mechanisms, the final product optimization and experimental verification of the predicted PSD for two examples.
However, the industrialisation of continuous processing is not trivial, as to achieve a high level of accuracy and control, a greater level of understanding during the phase of development and design is required.
To accelerate tech. Process simulation plays a key role in this new work flow, from process development to process design and process control in GMP facilities. We investigated and compared a pilot scale lyophilizer and a production scale lyophilizer. The initial comparison was based on water sublimation studies and evaluation of heat transfer coefficients. The calibration was performed by fitting the model predictions to experimental data from both lyophilizers.
The calibrated models may serve as a tool for eventual modification or optimization of the current cycle and also as a basis for a planned transfer of the existing cycle to another lyophilizer. Some examples will be also presented. Do we really need mathematical models in cell-based bioprocessing? Alex Kiparissides, UCL Despite the outstanding research developments in biotechnology, the sophisticated mathematical toolset that lead to the explosive growth of manufacturing capacity in the traditional chemical industries, known as Process Systems Engineering PSE , has not been widely applied to the bio-industry.
The multi-layered and complex nature of cellular function and regulation has dictated a paradigm of research in highly specialised fields.
This has led to the generation of in-depth, yet disjoint, knowledge. What is presently lacking are novel engineering approaches able to integrate, organise and guide experimental information across multiple scales of complexity, all the way from strain design to bioprocess optimization. What is the role if any of mathematical modelling in modern bioprocessing and what are some of the key challenges we need to overcome? Other features include improved robustness, including better sequential initialisation for complex flowsheets, more robust physprops Multiflash and gSAFT , as well improved ways of loading and managing libraries.
Laboratory to industrial reactor: model-based design, scale-up, and operational optimization of fixed bed catalytic reactors and processes Alejandro Cano, PSE Careful design of catalytic reactors, the workhorses of the chemicals industry, can reduce both the cost and risk associated with new designs, as well as reduce ownership costs significantly through better catalyst performance.
This presentations shows how it is now possible to perform detailed design of multitubular reactors taking into account multi-scale effects — from catalyst pore to industrial reactor scale — by combining high fidelity models with model-targeted experimentation. In this work, a predictive mathematical model which accounts for the effect of membrane pore-size distribution and operating conditions to predict the membrane partial wetting and CO 2 absorption from natural gas is developed and verified with the lab and pilot-scale MBC modules.
GSA is used to determine the membrane characteristics and operating conditions that have the largest impact on CO 2 removal performance and costs.
Then, the design and operation of the entire process are optimized, with the objective of minimizing the annualised cost, subject to a given CO 2 specification. However the complex interactions inherent in integration throw up a number of optimisation challenges. Typically, Linear Programming LP is the standard approach for site-wide optimisation. This has generally been acceptable for refinery applications, but it can easily fall down when petrochemical plants are included because such process systems behave in a highly non-linear way.
In practice, the fact that students have a remote web access, allowed them to use PYX4 solution where they wanted, i. The implementation of the solution without the intervention of IT support personnel , also represents a real advantage. We are impatiently waiting for future evolutions of the tool in order to extend the risks concept to process modelling.
Our students find the solution intuitive and very convenient. Project overview Context Process modelling is one of the essential tools for quality services, management controllers and internal auditors. Objectives To enable students to: master process analysis, essential for their future professional life focus on process modelling, and not on learning a software tool. Results What is the role of modelling and process management in business areas such as managerial controls and internal audits?
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