7 research outputs found
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Stability criterion for the intensification of batch processes with model predictive control
Thermal runaways in batch processes can lead to significant issues for safety and performance during normal operation in industry. This is usually circumvented by running such processes at lower temperatures than necessary, hence losing the opportunity to intensify production and therefore reduce reaction time. The detection of the thermal stability of batch systems can potentially be embedded in an advanced control scheme, therefore improving the performance by being able to intensify the process, achieving higher yields while keeping a stable operation.
The derivation of stability criterion K for high-order reactions is presented in this work, resulting in better control when embedded in Model Predictive Control (MPC) schemes than standard nonlinear MPC schemes, based on the work in Kahm and Vassiliadis (2018). The non-trivial extension of stability criterion K for multi-component reactions with application to MPC systems is discussed in detail. The logic and verification of the form of the resultant Damkohler number in particular is discussed and demonstrated with case studies. A comparison of various MPC schemes is presented, showcasing that the implementation using criterion K results in intensified processes kept stable at all times, whilst reducing computational cost with regards to standard nonlinear MPC schemes. Furthermore, reaction times are reduced by at least two-fold with respect to processes run at constant temperatures
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Thermal stability criterion of complex reactions for batch processes
Thermal stability of batch processes is a major factor for the safe and efficient production of polymers and pharmaceutical chemicals. The prediction of the thermal stability for such processes was shown in Kahm and Vassiliadis (2018d) to be unreliable with most stability criteria found in literature also presenting a novel criterion, K, which was shown to give reliable stability predictions for single reactions of higher order.
This work provides a detailed derivation for the generalization of thermal stability criterion K applied to reaction networks of arbitrary complexity, consisting of parallel and competing reactions of both exothermic and endothermic nature. The generalized thermal stability criterion K is then applied to Model Predictive Control (MPC) frameworks to intensify batch processes in a safe manner, reducing the time required to reach the target conversion. Several illustrative computational case studies are presented, highlighting the proposed methodology and verifying its validity
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Optimal Laypunov exponent parameters for stability analysis of batch reactors with Model Predictive Control
Thermal runaways in exothermic batch reactions are a major economic, health and safety risk in industry. In literature most stability criteria for such behaviour are not reliable for nonlinear non-steady state systems. In this work, Lyapunov exponents are shown to predict the instability of highly nonlinear batch processes reliably and are hence incorporated in
standard MPC schemes, leading to the intensification of such processes. The computational time is of major importance for systems controlled by MPC. The optimal tuning of the initial perturbation and the time frame reduces the computational time when embedded in MPC schemes for the control of complex batch reactions. The optimal tuning of the initial perturbation and time horizon, defining Lyapunov exponents, has not been carried out in literature so far and is here derived through sensitivity analyses. The computational time required for this control scheme is analysed for the intensification of complex reaction schemes
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Robust thermal stability for batch process intensification with model predictive control
Thermal runaways in exothermic batch reactors present major safety and economic issues for industry. Control systems currently used are not capable of detecting thermal runaway behaviour and achieve nominally safe operation by carrying out the reaction at a low temperature. Recently, improvements in safety and process intensity have been achieved by using Model Predictive Control (MPC) with embedded stability criteria. The reliance of this approach on accurate model predictions makes plantmodel mismatch a crucial issue. The most common source of plant-model mismatch is uncertainty of model parameters. Scenario-based MPC and worst case MPC are used with stability criterion K and Lyapunov exponents in this work. The effect of all uncertain parameters on thermal runaway potential can be identified easily for simulations in this work. Hence, worst case MPC results in a computationally more efficient control scheme than scenario-based MPC, whilst ensuring the same extent of safety and process intensification