Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

Optimisation and Decision Support during the Conceptual Stage of Building Design

Merged with duplicate record 10026.1/726 on 28.02.2017 by CS (TIS)Modern building design is complex and involves many different disciplines operating in a fragmented manner. Appropriate computer-based decision support (DS) tools are sought that can raise the level of integration of different activities at the conceptual stage, in order to help create better designs solutions. This project investigates opportunities that exist for using techniques based upon the Genetic Algorithm (GA) to support critical activities of conceptual building design (CBD). Collective independent studies have shown that the GA is a powerful optimisation and exploratory search technique with widespread application. The GA is essentially very simple yet it offers robustness and domain independence. The GA efficiently searches a domain to exploit highly suitable information. It maintains multiple solutions to problems simultaneously and is well suited to non-linear problems and those of a discontinuous nature found in engineering design. The literature search first examines traditional approaches to supporting conceptual design. Existing GA techniques and applications are discussed which include pioneering studies in the field of detailed structural design. Broader GA studies are also reported which have demonstrated possibilities for investigating geometrical, topological and member size variation. The tasks and goals of conceptual design are studied. A rationale is introduced, aimed at enabling the GA to be applied in a manner that provides the most effective support to the designer. Numerical experiments with floor planning are presented. These studies provide a basic foundation for a subsequent design support system (DSS) capable of generating structural design concepts. A hierarchical Structured GA (SGA) created by Dasgupta et al [1] is investigated to support the generation of diverse structural design concepts. The SGA supports variation in the size, shape and structural configuration of a building and in the choice of structural frame type and floor system. The benefits and limitations of the SGA approach are discussed. The creation of a prototype DSS system, abritrarily called Designer-Pro (DPRO), is described. A detailed building design model is introduced which is required for design development and appraisal. Simplifications, design rationale and generic component modelling are mentioned. A cost-based single criteria optimisation problem (SCOP) is created in which other constraints are represented as design parameters. The thesis describes the importance of the object-oriented programming (OOP) paradigm for creating a versatile design model and the need for complementary graphical user interface (GUI) tools to provide human-computer interaction (HCI) capabilities for control and intelligent design manipulation. Techniques that increase flexibility in the generation and appraisal of concept are presented. Tools presented include a convergence plot of design solutions that supports cursor-interrogation to reveal the details of individual concepts. The graph permits study of design progression, or evolution of optimum design solutions. A visualisation tool is also presented. The DPRO system supports multiple operating modes, including single-design appraisal and enumerative search (ES). Case study examples are provided which demonstrate the applicability of the DPRO system to a range of different design scenarios. The DPRO system performs well in all tests. A parametric study demonstrates the potential of the system for DS. Limitations of the current approach and opportunities to broaden the study form part of the scope for further work. Some suggestions for further study are made, based upon newly-emerging techniques

Investigating relationships between cost and CO₂ emissions in reinforced concrete structures using a BIM-based design optimisation approach

An integrated design approach for the cost and embodied carbon optimisation of reinforced concrete structures is presented in this paper to inform early design decisions. A BIM-based optimisation approach that utilises Finite Element Modelling (FEM) and a multi-objective genetic algorithm with constructability constraints is established for that purpose. A multilevel engineering analysis model is developed to perform structural layout optimisation, slab and columns sizing optimisation, and slab and columns reinforcement optimisation. The overall approach is validated using real buildings and the relationships between cost and carbon optimum solutions are explored. The study exhibits how cost effective and carbon efficient solutions could be obtained without compromising the feasibility of the optimised designs. Results demonstrate that the structural layout and the slab thickness are amongst the most important design optimisation parameters. Finally, the overall analysis suggests that the building form can influence the relationships between cost and carbon for the different structural components

Performance assessment and optimisation of a novel guideless irregular dew point cooler using artificial intelligence

Air Conditioners (ACs) are a vital need in modern buildings to provide comfortable indoor air for the occupants. Several alternatives for the traditional coolers are introduced to improve the cooling efficiency but among them, Evaporative Coolers (ECs) absorbed more attention owing to their intelligible structure and high efficiency. ECs are categorized into two types, i.e., Direct Evaporative Coolers (DECs) and Indirect Evaporative Coolers (IECs). Continuous endeavours in the improvement of the ECs resulted in development of Dew Point Coolers (DPCs) which enable the supply air to reach the dew point temperature. The main innovation of DPCs relies on invention of a M-cycle Heat and Mass Exchanger (HMX) which contributes towards improvement of the ECs’ efficiency by up to 30%. A state-of-the-art counter flow DPC in which the flat plates in traditional HMXs are replaced by the corrugated plates is called Guideless Irregular DPC (GIDPC). This technology has 30-60% more cooling efficiency compared to the flat plate HMX in traditional DPCs.Owing to the empirical success of the Artificial Intelligence (AI) in different fields and enhanced importance of Machine Learning (ML) models, this study pioneers in developing two ML models using Multiple Polynomial Regression (MPR), and Deep Neural Network (DNN) methods, and three Multi Objective Evolutionary Optimisation (MOEO) models using Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), and a novel bio-inspired algorithm, i.e., Slime Mould Algorithm (SMA), for the performance prediction and optimisation of the GIDPC in all possible operating climates. Furthermore, this study pioneers in developing an explainable and interpretable DNN model for the GIDPC. To this end, a game theory-based SHapley Additive exPlanations (SHAP) method is used to interpret contribution of the operating conditions on performance parameters.The ML models, take the intake air characteristic as well as main operating and design parameters of the HMX as inputs of the ML models to predict the GIDPC’s performance parameters, e.g., cooling capacity, coefficient of performance (COP), thermal efficiencies. The results revealed that both models have high prediction accuracies where MPR performs with a maximum average error of 1.22%. In addition, the Mean Square Error (MSE) of the selected DNN model is only 0.04. The objectives of the MOEO models are to simultaneously maximise the cooling efficiency and minimise the construction cost of the GIDPC by determining the optimum values of the selected decision variables.The performance of the optimised GIDPCs is compared in a deterministic way in which the comparisons are carried out in diverse climates in 2020 and 2050 in which the hourly future weather data are projected using a high-emission scenario defined by Intergovernmental Panel for Climate Change (IPCC). The results revealed that the hourly COP of the optimised systems outperforms the base design. Moreover, although power consumption of all systems increases from 2020 to 2050, owing to more operating hours as a result of global warming, but power savings of up to 72%, 69.49%, 63.24%, and 69.21% in hot summer continental, arid, tropical rainforest and Mediterranean hot summer climates respectively, can be achieved compared to the base system when the systems run optimally

Genetic programming application in predicting fluid loss severity.

Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is utilised to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are utilised. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations

Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted

Two-phase energy efficiency optimisation for ships using parallel hybrid electric propulsion system

Developing a green, intelligent, and efficient power system is an important way for the shipping industry to respond to increasingly stringent emission regulations, and to achieve improvements in energy conservation and efficiency. In this study, a two-phase energy efficiency optimisation method is proposed for reducing energy consumption. The method comprises a combination of speed optimisation and energy management. On the demand side, the minimum accumulated power consumption required by the propeller is set as the objective function for the speed optimisation model, whereas on the supply side, the lowest cost of energy consumed by the hybrid power system is set as the objective function for the energy management model. An inland parallel hybrid electric powered bulk carrier is selected for a case study of the two-phase energy efficiency optimisation method. The optimisation results are compared with the energy consumption data of a bulk carrier under normal working conditions. The results show that the proposed method can reduce the energy consumption by 2.60% and 9.86% in the westbound and eastbound voyages, respectively. Accordingly, this study can provide methodological support for inland hybrid-powered ships aiming to achieve intelligent energy efficiency management