A universal approach to phenomenological compartment models of unit operations

A compartment model describes the transmission of materials and/or energies through a unit operation, as a network of flow connected sub-volumes. Each sub volume is a well-mixed compartment, formed based on the identification of negligible gradients in the system properties of interest. Ordinary differential equations describe the temporal phenomenological and flow effects imposed on the variables (species mass and compartment enthalpy) of the system. Along with the associated initial values of the system, the variable ODE’s are numerically solved over time. Compartment modelling is widely used in chemical engineering as it provides a balance between flow and phenomena resolution, and solution times. From the profusion of compartment models in literature, the model development and thus solutions for this approach are both bespoke. Models are either hard coded ODE’s or built through the improvised use of available non-domain-specific tools; the former is especially error prone, and the latter restricts the model development to the capability of the tool used. For full modelling flexibility, modellers are required to have knowledge of software design for implementing and solving ODE’s with many variables. CompArt - A universal compartment modelling tool for unit operations has been developed in this work, this is formed of (i) a universal input language used to describe unit operation compartment models, (ii) complemented by an interpretation algorithm for the conversion of the model description into ODE’s for solving (utilising a universal compartment modelling equation set developed in this work) and, (iii) the wrapping of choice numerical solvers targeting stiff non-linear problems. This addition to the field circumvents the need for modelers to have specialised skills to utilise this modelling approach allows focus upon their domain of model development to take priority. The universal compartment modelling system, CompArt is validated against a benchmark set of 20 models ranging in structural make-up and applied phenomena

Pharmaceutical development and manufacturing in a Quality by Design perspective: methodologies for design space description

In the last decade, the pharmaceutical industry has been experiencing a period of drastic change in the way new products and processes are being conceived, due to the introduction of the Quality by design (QbD) initiative put forth by the pharmaceutical regulatory agencies (such as the Food and Drug Adminstration (FDA) and the European Medicines Agency (EMA)). One of the most important aspects introduced in the QbD framework is that of design space (DS) of a pharmaceutical product, defined as “the multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality”. The identification of the DS represents a key advantage for pharmaceutical companies, since once the DS has been approved by the regulatory agency, movements within the DS do not constitute a manufacturing change and therefore do not require any further regulatory post-approval. This translates into an enhanced flexibility during process operation, with significant advantages in terms of productivity and process economics. Mathematical modeling, both first-principles and data-driven, has proven to be a valuable tool to assist a DS identification exercise. The development of advanced mathematical techniques for the determination and maintenance of a design space, as well as the quantification of the uncertainty associated with its identification, is a research area that has gained increasing attention during the last years. The objective of this Dissertation is to develop novel methodologies to assist the (i) determination of the design space of a new pharmaceutical product, (ii) quantify the assurance of quality for a new pharmaceutical product as advocated by the regulatory agencies, (iii) adapt and maintain a design space during plant operation, and (iv) design optimal experiments for the calibration of first-principles mathematical models to be used for design space identification. With respect to the issue of design space determination, a methodology is proposed that combines surrogate-based feasibility analysis and latent-variable modeling for the identification of the design space of a new pharmaceutical product. Projection onto latent structures (PLS) is exploited to obtain a latent representation of the space identified by the model inputs (i.e. raw material properties and process parameters) and surrogate-based feasibility is then used to reconstruct the boundary of the DS on this latent representation, with significant reduction of the overall computational burden. The final result is a compact representation of the DS that can be easily expressed in terms of the original physically-relevant input variables (process parameters and raw material properties) and can then be easily interpreted by industrial practitioners. As regards the quantification of “assurance” of quality, two novel methodologies are proposed to account for the two most common sources of model uncertainty (structural and parametric) in the model-based identification of the DS of a new pharmaceutical product. The first methodology is specifically suited for the quantification of assurance of quality when a PLS model is to be used for DS identification. Two frequentist analytical models are proposed to back-propagate the uncertainty from the quality attributes of the final product to the space identified by the set of raw material properties and process parameters of the manufacturing process. It is shown how these models can be used to identify a subset of input combinations (i.e., raw material properties and process parameters) within which the DS is expected to lie with a given degree of confidence. It is also shown how this reduced space of input combinations (called experiment space) can be used to tailor an experimental campaign for the final assessment of the DS, with a significant reduction of the experimental effort required with respect to a non-tailored experimental campaign. The validity of the proposed methodology is tested on granulation and roll compaction processes, involving both simulated and experimental data. The second methodology proposes a joint Bayesian/latent-variable approach, and the assurance of quality is quantified in terms of the probability that the final product will meet its specifications. In this context, the DS is defined in a probabilistic framework as the set of input combinations that guarantee that the probability that the product will meet its quality specifications is greater than a predefined threshold value. Bayesian multivariate linear regression is coupled with latent-variable modeling in order to obtain a computationally friendly implementation of this probabilistic DS. Specifically, PLS is exploited to reduce the computational burden for the discretization of the input domain and to give a compact representation of the DS. On the other hand, Bayesian multivariate linear regression is used to compute the probability that the product will meet the desired quality for each of the discretization points of the input domain. The ability of the methodology to give a scientifically-driven representation of the probabilistic DS is proved with three case studies involving literature experimental data of pharmaceutical unit operations. With respect to the issue of the maintenance of a design space, a methodology is proposed to adapt in real time a model-based representation of a design space during plant operation in the presence of process-model mismatch. Based on the availability of a first-principles model (FPM) or semi-empirical model for the manufacturing process, together with measurements from plant sensors, the methodology jointly exploits (i) a dynamic state estimator and (ii) feasibility analysis to perform a risk-based online maintenance of the DS. The state estimator is deployed to obtain an up-to-date FPM by adjusting in real-time a small subset of the model parameters. Feasibility analysis and surrogate-based feasibility analysis are used to update the DS in real-time by exploiting the up-to-date FPM returned by the state estimator. The effectiveness of the methodology is shown with two simulated case studies, namely the roll compaction of microcrystalline cellulose and the penicillin fermentation in a pilot scale bioreactor. As regards the design of optimal experiments for the calibration of mathematical models for DS identification, a model-based design of experiments (MBDoE) approach is presented for an industrial freeze-drying process. A preliminary analysis is performed to choose the most suitable process model between different model alternatives and to test the structural consistency of the chosen model. A new experiment is then designed based on this model using MBDoE techniques, in order to increase the precision of the estimates of the most influential model parameters. The results of the MBDoE activity are then tested both in silico and on the real equipment

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Optimal design and control of mine site energy supply systems.

The mining sector has seen an increase in costs associated with the use of energy in recent decades. Due to lower ore grade, deeper mineralization, or more remote location new mines generally require more energy to produce the same amount of mineral. Mining operations require reliable and cost-effective energy supply, without which extraction becomes economically risky, as well as unsafe for miners. Commercial software and research-oriented computer models are now available to assist in the decision making process regarding the optimal selection of Energy Supply Systems (ESS) and associated costs. However, software and models present limitations: some are designed to minimize the cost of supplying only heat and electricity, while others are custom applications for the residential and commercial sectors. Most computer tools assume invariable operating conditions, e.g. energy supply and demand profiles that do not change throughout the lifetime of the mine, or conditions whose variations can be perfectly predicted. As a result, the optimization of ESS can yield designs that lack robustness to deal with real life, changing environments. Under the same approach, the Optimal Mine Site Energy Supply (OMSES) concept was originally developed as a deterministic mathematical programming tool to find the optimal combination of energy technologies and sources that could meet final energy demands. The solution also included the optimal operation strategy based on typical energy demands of a specific mine site. This thesis expands OMSES to address the robustness of the solution, by considering the uncertainty and variability of real operating conditions. A method is proposed herein, based on the optimal solution obtained by OMSES and utilizing Model Predictive Control (MPC). The MPC-based simulation under changing environmental conditions ensures that energy demands are met at all times, taking into account energy demands and supply forecast, as well as their inherent variability. Results show that near optimal, more robust design solutions are obtained when the system is simulated under uncertain, more realistic operational conditions, leaving MPC in charge of exploring under-capacity events and of redesigning the system to ensure feasibility with minimum cost increase. This new method has been termed MPC-OMSES dynamic redesign. This thesis also reports on research work to adapt OMSES formulation to account for varying demands throughout the life of the mine, as a consequence of the natural process of mine development and extraction, which means deeper operations over time. This process entails a progressive increase in energy demands, and therefore the energy supply system must be planned accordingly. The proposed Long Term OMSES (LTOMSES) shows the advantages of considering an investment plan for the ESS, especially in the case of capital-intensive renewable energy technologies. Other concepts that have been integrated in OMSES and are covered in this thesis include: (i) material flows with considerable impact in the energy consumption have been included in the mathematical formulation, in combination with the corresponding technologies, such as pumps, fans and mobile equipment; (ii) energy and material storage have been also included, along with complex utility tariff structures, and grid and pipeline extensions. More innovative and integrated solutions can be considered by expanding the feasibility region of the optimization problem, as shown in a case study covering the integration of battery-powered electric underground mobile equipment. Overall, this thesis provides insight and tools to assist engineers in the important task of designing comprehensive and cost-effective energy supply systems for underground mines. Future work suggested includes: the development of a methodology to design fully adaptive ESS (not considering a pre-existing optimal or sub-optimal design); the simultaneous optimization of the production plan (ore extracted per day) and the design and operation of the ESS; and a dynamic approach to review the investment plan in the face of long-term environmental operating conditions.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Course Manual Winter School on Structure and Functions of Marine Ecosystem: Fisheries

Marine ecosystems comprises of diverse organisms and their ambient abiotic components in varied relationships leading to an ecosystem functioning. These relationships provides the services that are essential for marine organisms to sustain in the nature. The studies examining the structure and functioning of these relationships remains unclear and hence understanding and modelling of the ecological functioning is imperative in the context of the threats different ecosystem components are facing. The relationship between marine population and their environment is complex and is subjected to fluctuations which affects the bottom level of an ecosystem pyramid to higher trophic levels. Understanding the energy flow within the marine ecosystems with the help of primary to secondary producers and secondary consumers are potentially important when assessing such states and changes in these environments. Many of the physiological changes are known to affect the key functional group, ie. the species or group of organisms, which play an important role in the health of the ecosystem. In marine environment, phytoplankton are the main functional forms which serves as the base of marine food web. Any change in the phytoplankton community structure may lead to alteration in the composition, size and structure of the entire ecosystem. Hence, it is critical to understand how these effects may scale up to population, communities, and entire marine ecosystem. Such changes are difficult to predict, particularly when more than one trophic level is affected. The identification and quantification of indicators of changes in ecosystem functioning and the knowledge base generated will provide a suitable way of bridging issues related to a specific ecosystem. New and meaningful indicators, derived from our current understanding of marine ecosystem functioning, can be used for assessing the impact of these changes and can be used as an aid in promoting responsible fisheries in marine ecosystems. Phytoplantkon is an indicator determining the colour of open Ocean. In recent years, new technologies have emerged which involves multidisciplinary activities including biogeochemistry and its dynamics affecting higher trophic levels including fishery. The winter school proposed will provide the insights into background required for such an approach involving teaching the theory, practical, analysis and interpretation techniques in understanding the structure and functioning of marine ecosystems from ground truth measurements as well as from satellite remote sensing data. This is organized with the full funding support from Indian council of Agricultural Research (ICAR) New Delhi and the 25 participants who are attending this programme has been selected after scrutiny of their applications based on their bio-data. The participants are from different States across Indian subcontinent covering north, east, west and south. They are serving as academicians such as Professors/ scientists and in similar posts. The training will be a feather in their career and will enable them to do their academic programmes in a better manner. Selected participants will be scrutinized initially to understand their knowledge level and classes will be oriented based on this. In addition, all of them will be provided with an e-manual based on the classes. All selected participants are provided with their travel and accommodation grants. The faculty include the scientists who developed this technology, those who are practicing it and few user groups who do their research in related areas. The programme is coordinated by the Fishery Resources Assessment Division of CMFRI. This programme will generate a team of elite academicians who can contribute to sustainable management of marine ecosystem and they will further contribute to capacity building in the sector by training many more interested researchers in the years to come

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Advanced Testing and Characterization of Bituminous Materials, Two Volume Set

Bituminous materials are used to build durable roads that sustain diverse environmental conditions. However, due to their complexity and a global shortage of these materials, their design and technical development present several challenges. Advanced Testing and Characterisation of Bituminous Materials focuses on fundamental and performance testin

Advanced Testing and Characterization of Bituminous Materials, Two Volume Set

Bituminous materials are used to build durable roads that sustain diverse environmental conditions. However, due to their complexity and a global shortage of these materials, their design and technical development present several challenges. Advanced Testing and Characterisation of Bituminous Materials focuses on fundamental and performance testin