Development and Validation of an In‐Line API Quantification Method Using AQbD Principles Based on UV‐Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

open access journalA key principle of developing a new medicine is that quality should be built in, with a thorough understanding of the product and the manufacturing process supported by appropriate process controls. Quality by design principles that have been established for the development of drug products/substances can equally be applied to the development of analytical procedures. This paper presents the development and validation of a quantitative method to predict the concentration of piroxicam in Kollidon® VA 64 during hot melt extrusion using analytical quality by design principles. An analytical target profile was established for the piroxicam content and a novel in‐line analytical procedure was developed using predictive models based on UV‐Vis absorbance spectra collected during hot melt extrusion. Risks that impact the ability of the analytical procedure to measure piroxicam consistently were assessed using failure mode and effect analysis. The critical analytical attributes measured were colour (L* lightness, b* yellow to blue colour parameters—in‐process critical quality attributes) that are linked to the ability to measure the API content and transmittance. The method validation was based on the accuracy profile strategy and ICH Q2(R1) validation criteria. The accuracy profile obtained with two validation sets showed that the 95% β‐expectation tolerance limits for all piroxicam concentration levels analysed were within the combined trueness and precision acceptance limits set at ±5%. The method robustness was tested by evaluating the effects of screw speed (150–250 rpm) and feed rate (5–9 g/min) on piroxicam content around 15% w/w. In‐line UV‐Vis spectroscopy was shown to be a robust and practical PAT tool for monitoring the piroxicam content, a critical quality attribute in a pharmaceutical HME process

JOINING SEQUENCE ANALYSIS AND OPTIMIZATION FOR IMPROVED GEOMETRICAL QUALITY

Disturbances in the manufacturing and assembly processes cause geometrical variation from the ideal geometry. This variation eventually results in functional and aesthetic problems in the final product. Being able to control the disturbances is the desire of the manufacturing industry. \ua0 Joining sequences impact the final geometrical outcome in an assembly considerably. To optimize the sequence for improved geometrical outcome is both experimentally and computationally expensive. In the simulation-based approaches, based on the finite element method, a large number of sequences need to be evaluated.\ua0 In this thesis, the simulation-based joining sequence optimization using non-rigid variation simulation is studied. Initially, the limitation of the applied algorithms in the literature has been addressed. A rule-based optimization approach based on meta-heuristic algorithms and heuristic search methods is introduced to increase the previously applied algorithms\u27 time-efficiency and accuracy. Based on the identified rules and heuristics, a reduced formulation of the sequence optimization is introduced by identifying the critical points for geometrical quality. A subset of the sequence problem is identified and solved in this formulation.\ua0 For real-time optimization of the joining sequence problem, time-efficiency needs to be further enhanced by parallel computations. By identifying the sequence-deformation behavior in the assemblies, black-box surrogate models are introduced, enabling parallel evaluations and accurate approximation of the geometrical quality. Based on this finding, a deterministic stepwise search algorithm for rapid identification of the optimal sequence is introduced.\ua0 Furthermore, a numerical approach to identify the number, location from a set of alternatives, and sequence of the critical joining points for geometrical quality is introduced. Finally, the cause of the various deformations achieved by joining sequences is identified. A time-efficient non-rigid variation simulation approach for evaluating the geometrical quality with respect to the sequences is proposed. \ua0 The results achieved from the studies presented indicate that the simulation-based real-time optimization of the joining sequences is achievable through a parallelized search algorithm and a rapid evaluation of the sequences. The critical joining points for geometrical quality are identified while the sequence is optimized. The results help control the assembly process with respect to the joining operation, improve the geometrical quality, and save significant computational time

Mastering Uncertainty in Mechanical Engineering

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering

Methodologies for the optimisation, control and consideration of uncertainty of reactive distillation

The work presented in this thesis is motivated by the current obstacles hindering the implementation of reactive distillation in industry, mainly related to the complexities of its design and control, as well as the impact of uncertainties thereupon. This work presents a rigorous methodology for the optimal design and control under uncertainty of reactive distillation. The methodology can also be used to identify and investigate mitigation strategies for process failures arising due to design and/or operation deficiencies under changed processing conditions, based on the evaluation of different design and/or control alternatives. The first step of the methodology is the simultaneous (MINLP) optimisation of the design and operation of a reactive distillation process superstructure, used to explore the possible steady-state design alternatives available, including ancillary equipment such as pre- and side-reactors, side-strippers and additional distillation columns, based on product-related constraints and a detailed objective cost function. The next step is the investigation of the dynamic control performance of this optimal system, where conventional and advanced process control strategies are considered in order to investigate how robust the system is towards operational disturbances, or whether revising the optimal steady-state design is required. As the optimisation depends heavily on accurate data for reaction kinetics and separation performance, the final step of the methodology is the evaluation of the impact of parameter uncertainty on the performance of the optimal controlled system, including redesigning the controlled system if required. The methodology is demonstrated using a number of industrially relevant case studies with different reaction and separation characteristics in order to investigate how these determine the design and control of an economically attractive and rigorous reactive distillation process. It is demonstrated that the process characteristics have a significant impact on the design of the system, and that auxiliary equipment may be required to meet production specifications and/or to ensure robust controlled behaviour. It is also shown that, under parameter uncertainty, an optimal controlled system may nevertheless face performance issues, and revising the design and/or operation of the process may be required in order to mitigate such situations

Self-resilient production systems : framework for design synthesis of multi-station assembly systems

Product design changes are inevitable in the current trend of time-based competition where product models such as automotive bodies and aircraft fuselages are frequently upgraded and cause assembly process design changes. In recent years, several studies in engineering change management and reconfigurable systems have been conducted to address the challenges of frequent product and process design changes. However, the results of these studies are limited in their applications due to shortcomings in three aspects which are: (i) They rely heavily on past records which might only be a few relevant cases and insufficient to perform a reliable analysis; (ii) They focus mainly on managing design changes in product architecture instead of both product and process architecture; and (iii) They consider design changes at a station-level instead of a multistation level. To address the aforementioned challenges, this thesis proposes three interrelated research areas to simulate the design adjustments of the existing process architecture. These research areas involve: (i) the methodologies to model the existing process architecture design in order to use the developed models as assembly response functions for assessing Key Performance Indices (KPIs); (ii) the KPIs to assess quality, cost, and design complexity of the existing process architecture design which are used when making decisions to change the existing process architecture design; and (iii) the methodology to change the process architecture design to new optimal design solutions at a multi-station level. In the first research area, the methodology in modeling the functional dependence of process variables within the process architecture design are presented as well as the relations from process variables and product architecture design. To understand the engineering change propagation chain among process variables within the process architecture design, a functional dependence model is introduced to represent the design dependency among process variables by cascading relationships from customer requirements, product architecture, process architecture, and design tasks to optimise process variable design. This model is used to estimate the level of process variable design change propagation in the existing process architecture design Next, process yield, cost, and complexity indices are introduced and used as KPIs in this thesis to measure product quality, cost in changing the current process design, and dependency of process variables (i.e, change propagation), respectively. The process yield and complexity indices are obtained by using the Stream-of-Variation (SOVA) model and functional dependence model, respectively. The costing KPI is obtained by determining the cost in optimizing tolerances of process variables. The implication of the costing KPI on the overall cost in changing process architecture design is also discussed. These three comprehensive indices are used to support decision-making when redesigning the existing process architecture. Finally, the framework driven by functional optimisation is proposed to adjust the existing process architecture to meet the engineering change requirements. The framework provides a platform to integrate and analyze several individual design synthesis tasks which are necessary to optimise the multi-stage assembly processes such as tolerance of process variables, fixture layouts, or part-to-part joints. The developed framework based on transversal of hypergraph and task connectivity matrix which lead to the optimal sequence of these design tasks. In order to enhance visibility on the dependencies and hierarchy of design tasks, Design Structure Matrix and Task Flow Chain are also adopted. Three scenarios of engineering changes in industrial automotive design are used to illustrate the application of the proposed redesign methodology. The thesis concludes that it is not necessary to optimise all functional designs of process variables to accommodate the engineering changes. The selection of only relevant functional designs is sufficient, but the design optimisation of the process variables has to be conducted at the system level with consideration of dependency between selected functional designs

The Optimization of Packaging System Process Parameters Using Taguchi Method

Packaging systems constitute substantially to product cost, its safety, and optimization. Unfortunately, no previous optimization studies have examined the packaging system in a bottling process plant for the unique, developing country environment. Consequently, the Taguchi method is applied to optimize a process plant's packaging system in a Nigerian plant's real-life situation. Optimal combinations of packaging system parameters that minimize product waste are created. An L4 (23) Taguchi orthogonal array was selected to analyze the data, and signal-to-noise ratios were computed for each experiment's run. Since the aim was to minimize beer waste, the ‘the-smaller-the-better’ signal-to-noise ratio was chosen in the analysis. S/N ratio plots revealed the optimum settings to obtain minimal product waste, namely, A2, B1, and C2 from the main effects plot for signal-to-noise ratios. A two-way ANOVA was performed on the significant factors to determine their percentage contributions to the response (product waste). Through Taguchi's innovative approach, the feasibility of optimizing the packaging process parameters was demonstrated and validated