Higher Order Predictive Functional Control versus dynamical matrix control for a milk pasteurisation process: Transfer function versus finite step response internal models

Predictive functional control (PFC), a model pre- dictive control algorithm, has been proven to be very suc- cessful in a wealth of industrial applications due to its many laudable attribute, such as its s implicity and intuitive appeal. For simple single input single output processes, PFC applica- tions use a first-order plus delay internal model and, as long as such models improve the control over classical control strategies, then their use remains justified. In this paper, a higher order internal PFC model is considered in order to reduce any possible plant-model mismatch, where the inter- nal model is formulated as a series of cascaded or parallel first-order systems. The control approach is compared to a more conventional over parameterized dynamical matrix control (DMC) approach, used extensively for Multi-Input Multi-Output systems in the petrochemical industry. This paper demonstrates the benefits of the PFC higher order formulation for a typical milk pasteurisation plant, with sig- nificant improvements in the variances of both controlled and manipulated variables when compared to a first-order PFC. In this aspect, the higher order controller competes well with DMC performances, however, using a much more sim- pler and compact internal model form

Predictive functional control for the temperature control of a chemical batch reactor

A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system

Design and Control of Power Converters 2020

In this book, nine papers focusing on different fields of power electronics are gathered, all of which are in line with the present trends in research and industry. Given the generality of the Special Issue, the covered topics range from electrothermal models and losses models in semiconductors and magnetics to converters used in high-power applications. In this last case, the papers address specific problems such as the distortion due to zero-current detection or fault investigation using the fast Fourier transform, all being focused on analyzing the topologies of high-power high-density applications, such as the dual active bridge or the H-bridge multilevel inverter. All the papers provide enough insight in the analyzed issues to be used as the starting point of any research. Experimental or simulation results are presented to validate and help with the understanding of the proposed ideas. To summarize, this book will help the reader to solve specific problems in industrial equipment or to increase their knowledge in specific fields

Self-Optimisation of Automated Continuous Reactors

The optimisation of problematic reaction steps in the synthesis of a drug compound is crucial for pharmaceutical process development. In recent traditions, this has carried out using design of experiments (DoE), which shows the key reaction variables and provides optimum reaction conditions. The process can require a lot of experiments and be time and resource consuming. The speed of optimisation experiments can be increased by using automated platforms complete with online analysis, which carry out reactions and acquire analytical samples without any human intervention. If these experiments can be carried out in continuous reactors then they will benefit from faster kinetics, enhanced heat and mass transfer, improved safety and higher productivity over their batch counterparts. An automated self-optimising flow reactor combines a continuous reactor with online analysis and feedback loop. The feedback loop contains full computerised control and monitoring of all equipment as well as a minimising algorithm, which will use the results from the online analysis to predict new optimum conditions. The technique has been shown to optimise the synthesis of small organic compounds but has, so far, yet to be widely used in pharmaceutical process development. This thesis has improved self-optimising technologies in order to make it a useful technique in pharmaceutical process development. First, the final bond forming step in the synthesis of an active pharmaceutical ingredient was optimised for yield. Studies were primarily carried out on a model compound in order to establish the correct reactor setup before transferring to the active compound, which found an optimum yield of 89%. The work also provided mechanistic evidence for generation of impurities. Next, response surface models were successfully fitted to the data obtained from a branch and fit algorithm optimisation of a Claisen-Schmidt condensation. In depth statistical calculations show how DoE models can be generated from self-optimisation data with good fit and predictability (R2 > 0.95, Q2 > 0.90), and with the aid of commercial DoE software. Further work developed the use of direct mass spectrometry (MS) as the online analytical method. The short method times and real-time analysis of MS allowed a steady state detection function to be built, followed by a linear calibration model of all the species in the amidation of a methyl ester. The reaction was optimised for yield using branch and fit algorithm, and DoE, with excellent agreement between the two techniques in both optimum conditions and responses. Finally, changes were made to the optimisation program to reduce the amount of material required for automated optimisations. Reaction pulses of sub-reactor volumes were pumped through the reactor, dispersed in a continuous phase of miscible solvent. Residence time distribution experiments were carried out to characterise the dispersion of the reactor and calculate the minimum reactor pulse volume. Optimisations were primarily carried out using pattern search algorithm and a multi-objective evolutionary algorithm, the latter of which generated a three target function optimum, reducing the amount of waste by 81%. Overall this work has shown how self-optimisation can be a valuable tool for pharmaceutical process development. The existing technique has been improved by demonstrating its use in the synthesis of pharmaceutical compounds, combining it with existing DoE techniques, adding new forms of online analysis, and reducing the amount of material required to deliver a multi-target optimum

Simulation environment for advanced control development of a multiple hearth furnace

The aim of this thesis is to setup a simulation environment that prepares the ground for Multiple Hearth Furnace (MHF) advanced process control development based on Economic Model Predictive Control (EMPC) and/or MPC techniques. Additionally, the interest is to design an Economic MPC for the Multiple Hearth Furnace, aiming to minimize energy consumption of the furnace while maintaining the specified product quality. The implementation of the EMPC requires a dynamic model which is simplified from a previously developed mechanistic model of the MHF. The simplified model is developed in the form of a nonlinear Hammerstein-Wiener model, which is linearized at every sampling time to carry out the state estimation and MPC optimization tasks. As the accuracy of the simplified process model is crucial for performance of the EMPC, the thesis aims to compare the simulation results of the mechanistic model and the simplified one. The comparison of the models show that the simplified model follows accurately the mechanistic model in all cases. A description of the process of interest is given, with an emphasis in outlining the overall control strategy currently implemented. Next the components of the EMPC design are illustrated, including the overall strategy, the cost function and the necessary models of the process. Afterwards an implementation algorithm is provided comprising all the elements of the design, in order to obtain the optimal control of the MHF. Finally, practical problems regarding industrial implementations of temperature control in the hearth 4 are discussed and further research items outlined

Intelligent control of industrial processes

A detailed survey of the field of intelligent control is presented. Current practices are reviewed and the need for a unifying framework to identify and strengthen the underlying core principles is postulated. Intelligent control is redefined to make explicit use of human systems in control as a reference model. Psychological theories of intelligent behaviour reveal certain basic attributes. From these a set of necessary and sufficient conditions for intelligent control are derived. Learning ability is identified as a crucial element. Necessary attributes for learning are prediction capabilities, internal world model, estimation of the model parameters, and active probing to reduce uncertainties. This framewoik is used to define a Learning Based Predictive Control (LBPC) strategy. LBPC is derived from Predictive Functional Control techniques with an adaptive layer implemented by recursive least squares. Improved performance above conventional adaptive control is demonstrated. Distributed parameter systems are identified as a suitable application area requiring an intelligent control approach. Such systems are invariably complex, ill-defined, and nonlinear. Plasticating extrusion processes are considered in particular. LBPC is applied to control of the primary loop to regulate melt temperature and pressure at the die. A novel control technique is proposed for dynamic profile control of extruder barrel wall temperature. This is a two-level hierarchical scheme combining the benefits of LBPC control blocks at the lowest level with decision logic operating at the higher level as a supervisor. This Logic Based Strategy allows multivariable control of non-square systems with more outputs than inputs. The application of LBS to an extruder is demonstrated