Search

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.

The extensive use of virtual prototyping methods has become an indispensable tool in the context of Model-Based Design of complex space missions. Modelling the behaviour of such missions often requires considering systems that are composed of physical subsystems (usually from different physical domains) together with computing and networking. Perpetual Labs is developing a new software platform for collaborative design of CPS called Cloud System Factory (CloudSF). It enables all the stakeholders of a complex engineering system to exchange system data and engineering artefacts independently of the specific tools that they are using. The proposed benefits of the CloudSF platform will be demonstrated and measured through the application of said platform to the model-based design and verification of a robotic system for on-orbit assembly of telescopic structures using an End-Over-End Walking robot, called the E-Walker.

MBSE aims to integrate engineering models across tools and domain boundaries to support traditional systems engineering activities (e.g., requirements elicitation and traceability, design, analysis, verification and validation). However, MBSE does not inherently solve interoperability with the multiple model-based infrastructures involved in a complex systems engineering project. The challenge is to implement digital continuity in the three dimensions of systems engineering: across disciplines, throughout the lifecycle, and along the supply chain. Space systems are ideal candidates for the application of MBSE and semantic modelling as these complex and expensive systems are mission-critical and often co-developed by multiple stakeholders. In this paper, the authors introduce the concept of Semantic Systems Engineering (SES) as an expansion of MBSE practices to include semantic modelling through SWTs. The paper also presents the progress and status of a novel Semantic Systems Engineering Ontology (SESO) in the context of a specific design case study – the Large Aperture Space Telescope mission.

The advent of digital twin technology enabled next steps in the evolution of the current state-of-the-art optimization solutions for prime movers in energy systems. Conventional offerings for operational optimization of prime movers were predominantly based on non-real-time and off-line solutions. Emerging digital twin technologies facilitated the next generation of simulation and optimization techniques, which in return enabled advanced utilization of engineering assets. These solutions exploit online functionalities which are distributed across the whole IoT chain consisting of Embedded, Edge, and Cloud computational platforms. In this contribution, we present various simulation and optimization techniques applicable to gas turbine systems. Gas turbines play a vital role in energy production as prime movers and they are extensively utilized as the workhorse within the power generation sector.

This paper describes a multi-degree-of-freedom model of a three-wheeled car, implemented in Matlab®. The purpose was to investigate the dynamics of the car (assumed to be rigid on its suspension) during cornering. While the problems associated with three-wheeled cars are well-known, much of the guidance in the literature and off-the-software assumes a conventional four-wheeled car. Consequently, the authors were approached with a battery-electric concept car which was thought to offer better performance than existing variants, because the use of hub motors lowered the centre of gravity and hence reduced rollover coefficient. However, simulation of the vehicle model in cornering shows that the concept is still prone to instability. Indeed, it suffers greater roll velocities than a comparable three-wheeled car with internal combustion engine, because the ratio of sprung to unsprung mass is significantly altered. This paper therefore recommends a programme of further simulations and model-based design changes to progress the concept to a marketable performance product.

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.

Logistics have been used to analyse agricultural operations, such as chemical application, mineral or organic fertilisation and harvesting-handling operations. Recently, due to national or European commitments concerning livestock waste management, this waste is being applied in many crops instead of other mineral fertilisers. The organic fertiliser produced has a high availability although most of the crops it is applied to have strict timeliness issues concerning its application. Here, organic fertilizer (as liquid manure) distribution logistic system is modelled by using a combined simulation and linear programming method. The method applies in certain crops and field areas taking into account specific agronomical, legislation and other constraints with the objective of minimising the optimal annual cost. Given their direct connection with the organic fertiliser distribution, the operations of cultivation and seeding were included. In a basic scenario, the optimal cost was assessed for both crops in total cultivated area of 120 ha. Three modified scenarios are presented. The first regards one more tractor as being available and provides a reduction of 3.8% in the total annual cost in comparison with the basic scenario. In the second and third modified scenarios fields having high nitrogen demand next to the farm are considered with one or two tractors and savings of 2.5% and 6.1%, respectively, compared to the basic scenario are implied. Finally, it was concluded that the effect of distance from the manure production to the location of the fields could reduce costs by 6.5%.

In the design of adaptive IIR filters, the multi-modal nature of the error surfaces can limit the use of gradient-based and other iterative search methods. Stochastic learning automata have previously been shown to have global optimisation properties making them suitable for the optimisation of filter coefficients. Continuous action reinforcement learning automata are presented as an extension to the standard automata which operate over discrete parameter sets. Global convergence is claimed, and demonstrations are carried out via a number of computer simulations. © 2002 Elsevier Science Ltd. All rights reserved.

Projects delivered in complex environments are often late, over-budget and provide fewer benefits than what were originally expected. Systems Engineering is the emerging paradigm in complex project environments to transform the governance from "project based" to "system based" and thereby increase the chance of holistic success. Systems Engineering is a multidisciplinary approach to enable the successful delivery of systems in complex environments through a comprehensive set of approaches, techniques and tools, initially developed in the USA after the Second World War. This paper focuses on how Systems Engineering can transform the governance from "project governance" to "system governance", improving the performance of projects delivered in a complex environment. This paper presents Systems Engineering tools and techniques focusing, in particular, on the most relevant for project management, project governance and stakeholder management. At the end it provides a rich research agenda for further studies. © 2013 Elsevier Ltd. APM and IPMA.

Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuable global optimization properties. Learning automata have, however, been criticized for having a relatively slow rate of convergence. In this paper, these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the chances of escaping local optima. The technique separates the genotype and phenotype properties of the GA and has the advantage that the degree of convergence can be quickly ascertained. It also provides the GA with a stopping rule. If the technique is applied to real-valued function optimization problems, then bounds on the range of the values within which the global optima is expected can be determined throughout the search process. The technique is demonstrated through a number of bit-based and real-valued function optimization examples.

Operations Management in Agriculture bridges the knowledge gap on operations management for agricultural machinery. It complements traditional topics (cost of using and choosing machinery) with advanced engineering approaches recently applied in agricultural machinery management (area coverage planning and sequential scheduling). The book covers new technologies in bio-production systems (robotics, IoT) and environmental compliance by employing a systems engineering perspective with focuses on sub-systems, including advanced optimization, supply chain systems, sustainability, autonomous vehicles and IT-driven decision-making. It will be a valuable resource for students studying decision-making and those working to improve the efficiency, effectiveness and sustainability of production through machinery choice.

There is a variety of crops that may be considered as potential biomass production crops. In order to select the best suitable for cultivation crop for a given area, a number of several factors should be taken into account. During the crop selection process, a common framework should be followed focussing on financial or energy performance. Combining multiple crops and multiple fields for the extraction of the best allocation requires a model to evaluate various and complex factors given a specific objective. This paper studies the maximisation of total energy gained from the biomass production by energy crops, reduced by the energy costs of the production process. The tool calculates the energy balance using multiple crops allocated to multiple fields. Both binary programming and linear programming methods are employed to solve the allocation problem. Each crop is assigned to a field (or a combination of crops are allocated to each field) with the aim of maximising the energy balance provided by the production system. For the demonstration of the tool, a hypothetical case study of three different crops cultivated for a decade (Miscanthus x giganteus, Arundo donax, and Panicum virgatum) and allocated to 40 dispersed fields around a biogas plant in Italy is presented. The objective of the best allocation is the maximisation of energy balance showing that the linear solution is slightly better than the binary one in the basic scenario while focussing on suggesting alternative scenarios that would have an optimal energy balance.

Biomass production systems include multiple-crops rotations, various machinery systems, diversified operational practices and several dispersed fields located in a range of distances between the various facilities (e.g., storage and processing facilities). These factors diversify the energy and cost requirements of the system. To that effect, assessment tools dedicated a single-crop production based on average standards cannot provide an insight evaluation of a specific production system, e.g., for a whole farm in terms of energy and cost requirements. This paper is the continuation of previous work, which presents a web-based tool for cost estimation of biomass production and transportation of multiple-crop production. In the present work, the tool is extended to additionally provide the energy balance of the examined systems. The energy input includes the whole supply chain of the biomass, namely crop cultivation, harvesting, handling of biomass and transportation to the processing facilities. A case study involving a real crop production system that feeds a biogas plant of 200 kW was selected for the demonstration of the tool’s applicability. The output of the tool provides a series of indexes dedicated to the energy input and balance. The presented tool can be used for the comparison of the performance, in terms of energy requirements, between various crops, fields, operations practices, and operations systems providing support for decisions on the biomass production system design (e.g., allocation of crops to fields) and operations management (e.g., machinery system selection).

Agriculture nowadays includes automation systems that contribute significantly to many levels of the food production process. Such systems include GPS based systems like auto-steering and Controlled Traffic Farming (CTF). These systems have led to many innovations in agricultural field area coverage design. Integrating these advancements, two different route planning designs, a traditional and an optimised one, are outlined and explained in this chapter. Four different machinery scenarios were tested in four fields each, and the main aim was to compare the two different route planning systems under economic criteria and identify the best operational route coverage design criterion. The results show that there are significant reductions in operational costs varying from 9 to 20%, depending on the specific machinery and field configurations. Such results show the considerable potential of advanced route planning designs and further optimization measures. They indicate the need for research efforts that quantify the operational and economic benefits by optimising field coverage designs in the headlands, turnings or obstacles avoidance according to the actual configuration to minimize the non-working activities and, as a consequence, the overall operational cost.

Various types of sensors technologies, such as machine vision and global positioning system (GPS) have been implemented in navigation of agricultural vehicles. Automated navigation systems have proved the potential for the execution of optimised route plans for field area coverage. This paper presents an assessment of the reduction of the energy requirements derived from the implementation of optimised field area coverage planning. The assessment regards the analysis of the energy requirements and the comparison between the non-optimised and optimised plans for field area coverage in the whole sequence of operations required in two different cropping systems: Miscanthus and Switchgrass production. An algorithmic approach for the simulation of the executed field operations by following both non-optimised and optimised field-work patterns was developed. As a result, the corresponding time requirements were estimated as the basis of the subsequent energy cost analysis. Based on the results, the optimised routes reduce the fuel energy consumption up to 8%, the embodied energy consumption up to 7%, and the total energy consumption from 3% up to 8%

Various crops can be considered as potential bioenergy and biofuel production feedstocks. The selection of the crops to be cultivated for that purpose is based on several factors. For an objective comparison between different crops, a common framework is required to assess their economic or energetic performance. In this paper, a computational tool for the energy cost evaluation of multiple-crop production systems is presented. All the in-field and transport operations are considered, providing a detailed analysis of the energy requirements of the components that contribute to the overall energy consumption. A demonstration scenario is also described. The scenario is based on three selected energy crops, namely Miscanthus, Arundo donax and Switchgrass. The tool can be used as a decision support system for the evaluation of different agronomical practices (such as fertilization and agrochemicals application), machinery systems, and management practices that can be applied in each one of the individual crops within the production system

Supply Chain Management for Sustainable Food Networks provides an up-to-date and interdisciplinary framework for designing and operating sustainable supply chains for agri-food products. Focus is given to decision-making procedures and methodologies enabling policy-makers, managers and practitioners to design and manage effectively sustainable agrifood supply chain networks. Authored by high profile researchers with global expertise in designing and operating sustainable supply chains in the agri-food industry, this book: Features the entire hierarchical decision-making process for managing sustainable agrifood supply chains. Covers knowledge-based farming, management of agricultural wastes, sustainability, green supply chain network design, safety, security and traceability, IT in agrifood supply chains, carbon footprint management, quality management, risk management and policy- making. Explores green supply chain management, sustainable knowledge-based farming, corporate social responsibility, environmental management and emerging trends in agri-food retail supply chain operations. Examines sustainable practices that are unique for agriculture as well as practices that already have been implemented in other industrial sectors such as green logistics and Corporate Social Responsibility (CSR). Supply Chain Management for Sustainable Food Networks provides a useful resource for researchers, practitioners, policy-makers, regulators and C-level executives that deal with strategic decision-making. Post-graduate students in the field of agriculture sciences, engineering, operations management, logistics and supply chain management will also benefit from this book.

Hybrid mathematical models typically have discontinuous inputs, or are visualised as state machines or pertri-nets comprising continuous models linked by discontinuous mappings. The analysis and simulation of hybrid (or nonsmooth dynamical) models is plagued with difficulty, necessitating careful consideration of energy losses and state reinitialisation on commutation. The author proposes and alternative model, where states are discontinuous. The engineer familiar with techniques such as signal flow graphs or bond graphs can clearly visualise discontinuities as breaks (or joins) in power flow between parts of the model. A mixed-Boolean state equation can be derived which reflects the physics of switching behaviour. This has two advantages: it can be simulated without the need for state reinitialisation algorithms, and it can be analysed for structural control properties to show how they change with commutation.

Since its inception, the Sustainable Development Division (SDD) has run a track in the Institute of Industrial and Systems Engineering (IISE) Annual Conference. Different topics have been associated to this track. This indicates that a vast variety of ideas have been associated to the effort of developing sustainable Industrial and Systems Engineering (ISE) practices. These works have been revisited and categorized utilizing the Triple Bottom Line framework by topics and countries of origin, in an effort to recognize potential patterns. Findings suggest that establishing a unique definition for 'sustainability' in the context of IISE seems a futile enterprise. Increasing the variety of responses, rather than reducing it, appears to be a better strategy to disseminate principles, particularly those from the United Nations 2030 sustainable development goals among IISE professionals.

Hybrid mathematical models are often represented as continuous functions with discontinuous inputs, or they are visualised as state machines or petri-nets comprising continuous models linked by discontinuous mappings. The analysis and simulation of hybrid (or nonsmooth dynamical) models is plagued with difficulty, necessitating careful consideration of energy losses and state reinitialisation on commutation. The author proposes an alternative model, where states are discontinuous. The engineer familiar with techniques such as signal flow graphs or bond graphs can clearly visualise discontinuities as breaks (or joins) in power flow between parts of the model. A mixed-Boolean state equation can be derived which reflects the physics of switching behaviour. This has two advantages: first, by considering the physics incrementally about the discontinuity it can be simulated without the need for state reinitialisation algorithms, and second, it can be analysed for structural control properties to show how they change with commutation.

The Sustainable Development Division of IIE has been defining and refining its body of knowledge framework since its recent founding. By incorporating frameworks and thoughts from professional and governmental organizations, as well as from research and industry, the leadership team of the Sustainable Development Division proposes a framework to define its body of knowledge within the Industrial Engineering universe. A variety of sustainability constructs including but not limited to triple-bottom-line, circles of sustainability, applications of lean systems methods in environmental improvement, kaizen, etc., are discussed and integrated to create a new proposed body of knowledge framework for the Sustainable Development Division of IIE.

In this paper, in order to address the well-known ‘sensitivity’ problems associated with K-means clustering, a real-coded Genetic Algorithms (GA) is incorporated into K-means clustering. The result of the hybridisation is an enhanced search algorithm obtained by incorporating the local search capability rendered by the hill-climbing optimisation with the global search ability provided by GAs. The proposed algorithm has been compared with other clustering algorithms under the same category using an artificial data set and a benchmark problem. Results show, in all cases, that the proposed algorithm outperforms its counterparts in terms of global search capability. Moreover, the scalability of the proposed algorithm to high-dimensional problems featuring a large number of data points has been validated using an application to compress field data sets from sub-15MW industry gas turbines, during commissioning. Such compressed field data is expected to result in more efficient and more accurate sensor fault detection.

This paper addresses the problem of fault detection and isolation for a particular class of discrete event dynamical systems called hierarchical finite state machines (HFSMs). A new version of the property of diagnosability for discrete event systems tailored to HFSMs is introduced. This notion, called L1-diagnosability, captures the possibility of detecting an unobservable fault event using only high level observations of the behavior of an HFSM. Algorithms for testing L1-diagnosability are presented. In addition, new methodologies are presented for studying the diagnosability properties of HFSMs that are not L1-diagnosable. These methodologies avoid the complete expansion of an HFSM into its corresponding flat automaton by focusing the expansion on problematic indeterminate cycles only in the associated extended diagnoser. © 2008 Springer Science+Business Media, LLC.

The primary objective of this paper is to introduce a new holistic approach to the design of alloy steels based on a biologically inspired multi-objective immune optimisation algorithm. To this aim, a modified population adaptive based immune algorithm (PAIA2) and a multi-stage optimisation procedure are introduced, which facilitate a systematic and integrated fuzzy knowledge extraction process. The extracted (interpretable) fuzzy models are able to fully describe the mechanical properties of the investigated alloy steels. With such knowledge in hand, locating the ‘best’ processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels is possible. The research has also enabled to unravel the power of multi-objective optimisation (MOP) for automating and simplifying the design of the heat treated alloy steels and hence to achieve ‘right-first-time’ production.

Controlled junctions with dynamic causality can be used to generate mixed-Boolean mathematical models. Several variations on the Switched or Hybrid Bond Graph have already been proposed, with none reaching common usage. The motivation for suggesting this approach was to develop a general method for adoption by practicing engineers, which is intuitive, adheres to the principles of idealized physical modelling and facilitates both structural analysis and efficient simulation. This paper revisits the classical example of a bouncing ball in order to discuss the advantages and disadvantages of such an approach with reference to the body of literature on hybrid models and nonsmooth dynamics. A switching model (as opposed to an impulse model) is generated which is both stiff and contains kinematic constraints, making it problematic to simulate. However, the method facilitates model simplification and the derivation of a coefficient of restitution, allowing Newton’s collision law to be applied. The resulting model simulates efficiently and well, without the need for parasitic elements or state reinitialization algorithms.

Researchers explored studies carried out and discussed during the workshop entitled 'Food for Thought: Designing for Critical Reflection on Food Practices' at the 2012 Designing Interactive Systems Conference held in Newcastle upon Tyne, UK. The workshop brought together people from industry and academia, across disciplines including HCI, architecture, and psychology. These professionals considered the challenges, successes, practices, and future directions of designing digital interactive systems to engage people in critical reflection toward positive changes in their everyday food practices. The participants wanted to find ways to promote more environmentally aware, socially inclusive, and healthier food practices through critical reflection. Critical reflection referred to the kind of thinking that consisted of turning a subject over in the mind and giving it serious and consecutive consideration.

The paper outlines a systematic method for controlling suspension deflection offset and drift, that can arise when an actively sprung vehicle encounters a constant gradient or a long wavelength road surface undulation. A brief review of the problem is given and it is shown that the inherent filtering effects of a standard Kalman observer does not sufficiently address the issue. An expanded Kalman filter is introduced to estimate the underlying vertical road velocity, and this is applied within a linear state feedback controller. Its effect on suspension performance is shown to be advantageous when compared with simpler state feedback and integral control techniques.

Both stochastic learning automata and genetic algorithms have previously been shown to have valuable global optimization properties. Learning automata have however been criticized for their perceived slow rate of convergence. In this paper these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the escape from local minima. The technique separates the genotype and phenotype properties of the genetic algorithm and has the advantage that the degree of convergence can be quickly ascertained. It also provides the genetic algorithm with a stopping rule and enables bounds to be given on the parameter values obtained.

In the design of adaptive IIR filters, the multi-modal nature of the error surfaces can limit the use of gradient-based and other iterative search methods. Stochastic learning automata have previously been shown to have global optimisation properties making them suitable for the optimisation of filter coefficients. Continuous action reinforcement learning automata are presented as an extension to the standard automata which operate over discrete parameter sets. Global convergence is claimed, and demonstrations are carried out via a number of computer simulations.

This workshop is a continuation and extension to the successful past workshops including 4, 5, 6. The workshop addresses the opportunities and challenges for the design of digital interactive systems that engage individuals in critical reflection on their everyday food practices - including designing for engagement in more environmentally aware, socially inclusive, and healthier behaviour. These three themes represent the focus of much recent HCI work related to food. The workshop aims to further the conversation on these themes through understanding specifically how the process of critical reflection can be encouraged by interactive technology. While the focus will be on food as an application area, the intention is to also explore, more generally, how the process of critical reflection can be facilitated through interactive technology. The workshop provides a unique forum to discuss existing theoretical and pragmatic approaches, and to envision novel ways to design technology that encourages sustained critical reflection. © 2012 ACM.

A reinforcement learning strategy is applied to the problem of the dynamic roll control of a full-body vehicle system fitted with semi-active suspension under digital control. The simulation model used in this study is based upon realistic vehicle hardware. Prior engineering knowledge of the non-linear actuation system is used to develop a control structure. Parameters in this structure are then obtained using Continuous Action Reinforcement Learning Automata (CARLA), an extension of the interconnected learning automata methodology. No model-based information is used in the controller synthesis.

A landing gear is a good example of a highly nonlinear multidisciplinary system. Using hybrid modelling, nonlinear behaviour can be abstracted to discontinuous behaviour. Bond graphs are an established and intuitive method for constructing mathematical models of multiphysics dynamic systems. The Hybrid Bond Graph is therefore used here to demonstrate the modelling of a landing gear drop test. A distinction is drawn between structural and parametric discontinuities, represented using controlled junctions and elements. Causally static and dynamic bond graphs are compared. A mixed Boolean mathematical model is obtained from the causally dynamic graph.

A wide range of modeling techniques is available to the engineer. The objective of this paper is to compare some typical modeling techniques for the simulation of a multidomain mechatronic system. Usual dynamic modeling methods, such as block diagrams and iconic diagrams, can cause problems for the engineer. Differential algebraic equations (DAEs) and algebraic loops can significantly increase simulation times and cause numeric errors. Bond graphs are less common in industry, and are presented here as a method which allows the engineer to easily identify causal loops and elements in differential causality. These can indicate DAEs in the underlying equations. An aircraft landing gear is given as an example of a multi-domain system, and is modeled as a block diagram, an iconic diagram and as a bond graph. The time to construct the model, time to solve and problems faced by the analyst are presented. Bond graphs offer distinct advantages in terms of the ease of implementing algebraic equations and visibility of causality. The time taken to model a system can be significantly reduced and the results appear free from computational errors. Bond graphs are therefore recommended for this type of multi-domain systems analysis

Engineering systems are frequently abstracted to models with discontinuous behaviour (such as a switch or contact), and a hybrid model is one which contains continuous and discontinuous behaviours. Bond graphs are an established physical modelling method, but there are several methods for constructing switched or ‘hybrid’ bond graphs, developed for either qualitative ‘structural’ analysis or efficient numerical simulation of engineering systems. This article proposes a general hybrid bond graph suitable for both. The controlled junction is adopted as an intuitive way of modelling a discontinuity in the model structure. This element gives rise to ‘dynamic causality’ that is facilitated by a new bond graph notation. From this model, the junction structure and state equations are derived and compared to those obtained by existing methods. The proposed model includes all possible modes of operation and can be represented by a single set of equations. The controlled junctions manifest as Boolean variables in the matrices of coefficients. The method is more compact and intuitive than existing methods and dispenses with the need to derive various modes of operation from a given reference representation. Hence, a method has been developed, which can reach common usage and form a platform for further study.

A new optimal design method and a systematic scheduling approach for a laboratory-scale Hot-Rolling Mill are presented. The proposed design is based upon 1. metallurgical principles, which sufficiently consider the behaviour of workpiece material and the mechanics of the manufacturing process and 2. a modified version for multi-objective optimization of a Population Adaptive based Immune Algorithm (PAIA), physically-based models and symbiotic modelling approach to carry-out an optimal search for the best microstructural parameters. This methodology processes adequate capabilities for finding effective and best microstructural parameters such as the ferrite grain size and the volume fraction of pearlite that satisfy the requirements for mechanical properties. Hence, the overarching aim of this research work is to integrate knowledge about both the stock and the rolling process to find optimal hot-deformation profiles that will be used as information in order to compute the most suitable rolling schedule and systemise the optimal route for processing and achieve a ‘right-first-time’ production of the desired properties.

The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.

This study explored development of a Gas Turbine Digital Twin based on Real-Time (Embedded \ Edge) high speed computing, which can be leveraged with IOT Cloud Platforms. Proposed solutions are provided in a form of modular software architecture for a range of platforms (Embedded \ PLC Based, Edge \ PC Based, Web \ Cloud Based) and with corresponding functionalities to support model-based control strategies (trip prevention & performance optimization) and advanced gas turbine health management

—In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data.

System Dynamics (SD) is a discipline helpful to recognize counterintuitive behaviors. By modeling and simulating different scenarios, complex situations can be studied and analyzed. Conceding that these activities may help on improving human action, the difficulty would strive on how to expand its use as it seems difficult to be caught by students, our future professionals. This paper elaborates on this by proposing an approach based on looking at SD as a problem structuring method (PSM), rather than a problem solving tool. Instances of the procedure are provided from a SD course delivered to undergraduate students in a Mexican university.

The global agri-food sector is responsible for c.30% of greenhouse gas (GHG) emissions (13.7Gt CO2e pa1) and 60% of lost nature around the world 2. However, Lapwing believes there is a better way. To hit Net Zero, carbon emissions from every sector of the economy need to be abated and carbon sequestration implemented to remove both past and difficult to remove emissions whilst protecting our precious natural environment. Lapwing has developed “Reverse Coal”, which both sequesters and abates significant quantities of carbon, and also produces food with measurable positive environmental and social impact: • Carbon sequestered and secured in a concentrated permanent store • Scaled abatement of emissions from lowland peat • Biodiversity enhanced • Water quality improvements • Flood alleviation protecting communities • Resilient production of healthy food, adapted to accommodate future climate change • High skilled full-time jobs replacing zero hours seasonal contracts Reverse Coal was born out of a necessity to develop a transition plan for Pollybell Farms (farming operator of The Lapwing Estate) to ensure food production could continue into the future without having an irreversible, negative impact on the environment but rather a positive one. Lapwing and Pollybell Farms have received recognition from the Rt Hon George Eustice MP, Secretary of State for Environment, Food and Rural Affairs, for our innovative land management techniques addressing climate change: “For some [climate change] will mean maximising food production from the most productive soils, but in new ways such as Pollybell Farms, which covers 5,000 acres straddling Nottinghamshire, Lincolnshire and Yorkshire and has been developing a totally new way of addressing their low-lying peat land to ensure both resilience and environmental benefit. Many of the country’s leading producers of fresh produce on our grade one fen soils are starting to think creatively about how they can manage their most valuable as