Identification Of Critical Parameters Research Articles

Identification of Critical Parameters for the Design of Energy Management Algorithms for Li-Ion Batteries Operating in PV Power Plants

Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step.

Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation.

Acoustic-electroelastic interactions in ultrasound energy transfer systems: Reduced-order modeling and experiment

Contactless ultrasound energy transfer from a piezoelectric cylinder or disk subjected to forced vibrations to a piezoelectric receiver offers the capability of safely transferring energy to sensors and devices, which is of great interest in different applications. Physical processes supporting ultrasonic energy transfer include piezoelectric-generated vibrations at a transmitting element, piezoelectric transduction of elastic vibrations at a receiving element, acoustic wave propagation, and acoustic-structure interactions at the surfaces of the transmitting and receiving elements. Considering these processes, we present an experimentally-validated multi-physics model that fills a knowledge gap in terms of accurately representing the fluid-loaded response of piezoelectric disks, usually used in ultrasonic energy transfer as a cylindrical transmitting source-cylindrical receiver combination. First, we derive the governing equations using the generalized Hamilton's principle and solve them using the finite element method. Second, we compute the surface pressure distribution due to acoustic-structure interactions under resonance conditions. We then use the mode shapes and surface pressure distributions obtained from the finite element model in conjunction with the matrix governing equations to develop a reduced-order model with quantified reactive and resistive parts of the acoustic radiation impedance. The developed reduced-order model is then experimentally validated by comparing the electrical impedance of four different piezoelectric disks having various aspect ratios. We also discuss how the proposed approach should be utilized to develop reduced-order model for piezoelectric receivers. The presented approach and reduced-order model allow for accurate identification of critical parameters that govern acoustic energy transfer between piezoelectric disks, which is crucial for designing efficient ultrasonic acoustic energy transfer systems.

A Theoretical Solution for Thermal Warpage of Flip-Chip Packages

The control of thermally induced warpage on electronic packages is an important issue during their fabrication process and assembly. Apart from a commonly used finite-element method and experimental methods for thermally induced warpage analysis of the packages, it still needs a fundamental theory to provide an insight of mechanics to assist and further check the finite-element and experiment-obtained results, especially for critical parameter identification. This article aims to take a close look into the well-known, closed-form Suhir theory for calculating thermally induced deformation of die–substrate assembly and correlate it with a finite-element analysis and further to propose a new Suhir-solution-based theory for predicting the thermal deformation of flip-chip packages. Through this article, the shear-effect region has been found, and its effect on curvature distribution over entire package length and corresponding warpage has been examined in terms of elastic modulus and thickness of underfills. The other parameters such as extended substrate and fillets of underfill are also studied. The limitation and feasibility of the theoretical solution to thermal deformation (warpage) prediction of flip-chip packages are discussed in detail by comparing with the finite-element and moire experimental results in this article.

Aqueous gelcasting of CeO2 ceramics using water-soluble epoxide

Manufacturing of CeO2 ceramics by an aqueous gelcasting shaping process was studied. This study has focused on the identification of critical parameters that influence the properties of suspensions and CeO2 ceramics. It is shown that ethylene glycol diglycidyl ether (EGDGE) and 3,3-diaminodipropylamine (DPTA) are suitable gelling agents for gelcasting process. This system has many advantages as low toxicity, limited impact on suspensions viscosity, and it does not disturb CeO2 dispersion by ammonium polyacrylate (PAA) in water. As a result, suspensions with high solid loading of 50 vol% were obtained. Solid loading appears as a critical parameter to control suspensions rheology, casting feasibility and final microstructural features of CeO2 ceramics. Finally, CeO2 ceramics with high relative density (>96%), good surface finish and complex shape were manufactured by gelcasting and natural sintering at 1500 °C for 1 h.

Investigation and modeling of direct toppling using a three-dimensional distinct element approach with incorporation of point cloud geometry

Block toppling instability can be a common problem in natural rock masses, especially in mining environments where excavation activity may trigger discontinuity-controlled instability by modifying the natural slope geometry. Traditional investigations of block toppling failure consider classic kinematic analyses and simplified two-dimensional limit equilibrium methods. This approach is still the most commonly adopted, but the simple two-dimensional conceptual model may often oversimplify the instability mechanisms, ignoring potential critical factors specifically related to the three-dimensional geometry. This paper uses a three-dimensional distinct element method approach applied to an example case study, identifying the critical parameters that influence direct toppling instability in an open pit environment. Terrestrial laser scanning was used to obtain detailed three-dimensional geometrical information of the slope face geometry for subsequent stability analyses. A series of sensitivity analyses on critical parameters such as friction angle, discontinuity shear and normal stiffness, discontinuity spacing, and orientation was performed, using simple conceptual three-dimensional numerical modeling. Results of the analyses revealed the importance of undertaking three-dimensional analyses for direct toppling investigations that allow identification of critical parameters. A three-dimensional distinct element analysis was then performed using a more realistic complex volumetric mesh model of the case study slope which confirmed the previous modeling results but also identified unstable blocks in high slope angle areas, providing useful information for life of mine design. The paper highlights the importance of slope geometry and fracture network orientation on potential slope instability mechanisms.

Identification of critical strains for the random cellular automata finite element failure model based on in-situ tensile test

The work is focused on the concept of identification of critical parameters of the random cellular automata finite element (RCAFE) fracture model. The investigated parameters are related to the fracture initiation criteria at the micro scale level for further simulation of the dual phase steel behaviour under deformation conditions. Parameters for brittle and ductile fractures initiation are identified based on the close combination of experimental and numerical in-situ tensile tests. Concept of the RCAFE model as well as details of proposed identification methodology with examples of obtained results are presented within the paper.

Improving bioreactor production of a recombinant glycoprotein in Escherichia coli: Effect of specific growth rate on protein glycosylation and specific productivity.

In the last decades bacterial glycoengineering emerged as a new field as the result of the ability to transfer the Campylobacter jejuni N- glycosylation machinery into Escherichia coli for the production of recombinant glycoproteins that can be used as antigens for diagnosis, vaccines, and therapeutics. However, the identification of critical parameters implicated in the production process and its optimization to jump to a productive scale is still required. In this study, we developed a dual expression glycosylation vector for the production of the recombinant glycoprotein AcrA-O157, a novel antigen that allows the serodiagnosis of the infection with enterohemorrhagic E. coli O157 in humans. Volumetric productivity was studied in different culture media and found that 2xYP had 6.9-fold higher productivity than the extensively used LB. Subsequently, bioreactor batch and exponential-fed-batch cultures were designed to determine the influence of the specific growth rate (μ) on AcrA-O157 glycosylation efficiency, production kinetics, and specific productivity. At μmax , AcrA glycosylation with O157-polysaccharide and the specific synthesis rate were maximal, constituting the optimal physiological condition for AcrA-O157 production. Our findings should be considered for the design, optimization, and scaling up of AcrA-O157 production and other recombinant glycoproteins attractive for industrial applications.

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviors of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index have been chosen as the relevant stability indices for voltage stability, small-disturbance stability, and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and, hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behavior due to its correlation with other influential parameters.

Comprehensive modelling and simulation towards the identification of critical parameters for evaluation of exoskeleton-centred workplaces

Workers are key enablers of flexibility and productivity in Manufacturing, especially in processes where full automation is not feasible. Such workplaces are often characterized by manual manipulation of heavy loads, hazardous conditions and high level of vibrations. Exoskeletons, fusing flexibility, intelligence and human-centered control with the high payload, endurance, precision and sensor-based guidance represent enabling technologies to cope this challenge. In this paper critical parameters for the evaluation of exoskeleton-centered workplaces are identified, as core elements of the methodology for evaluating the exoskeleton-centered workplaces. Comprehensive modelling, simulation and analysis of ergonomics and process parameters represent the foundations of the methodology.

Optimization of Machine Learning in Predicting Fracture Behaviour of Materials in AI

Machine learning has revolutionized various fields, including material science and engineering, by enabling accurate predictions and optimization of complex phenomena. One such area is the prediction of fracture behaviour in materials, which plays a crucial role in ensuring structural integrity and safety in numerous applications. This abstract focuses on the optimization of machine learning techniques for predicting fracture behaviour in materials using artificial intelligence (AI). The primary objective of this research is to develop a robust and efficient machine learning model that can accurately predict the fracture behaviour of materials. To achieve this, a comprehensive dataset comprising various material properties, such as strength, ductility, and microstructural features, is collected. Additionally, fracture-related data, including fracture toughness, crack propagation rates, and failure modes, are also incorporated into the dataset. Several machines learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are employed to train and evaluate the predictive models. The models are optimized by tuning hyperparameters and selecting the most relevant features through feature selection techniques. Furthermore, advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, are utilized to enhance the performance of the machine learning models.
 To ensure the generalizability and robustness of the developed models, cross-validation techniques and extensive testing on independent datasets are conducted. The accuracy and performance of the models are assessed through various evaluation metrics, such as mean squared error, accuracy, and precision-recall curves. Comparative analyses are performed to determine the most suitable machine learning algorithm for predicting fracture behaviour in materials. The results demonstrate that the optimized machine learning models exhibit high accuracy and reliability in predicting fracture behaviour. The developed models can effectively capture the complex relationships between material properties and fracture characteristics, enabling the identification of critical parameters that influence fracture behaviour. This research contributes to the advancement of AI in material science and engineering by providing valuable insights into the prediction and optimization of fracture behaviour in materials.
 In the optimization of machine learning techniques for predicting fracture behaviour in materials using AI. The developed models offer accurate predictions and valuable insights into the fracture characteristics of materials, aiding in the design and optimization of structural components with enhanced safety and performance. The application of machine learning in material science continues to evolve, and this research paves the way for further advancements in the field.

Drug-Loaded Biomimetic Ceramics for Tissue Engineering.

The mimesis of biological systems has been demonstrated to be an adequate approach to obtain tissue engineering scaffolds able to promote cell attachment, proliferation, and differentiation abilities similar to those of autologous tissues. Bioceramics are commonly used for this purpose due to their similarities to the mineral component of hard tissues as bone. Furthermore, biomimetic scaffolds are frequently loaded with diverse therapeutic molecules to enhance their biological performance, leading to final products with advanced functionalities. In this review, we aim to describe the already developed bioceramic-based biomimetic systems for drug loading and local controlled release. We will discuss the mechanisms used for the inclusion of therapeutic molecules on the designed systems, paying special attention to the identification of critical parameters that modulate drug loading and release kinetics on these scaffolds.

Estimation of the cancer risk induced by therapies targeting stem cell replication and treatment recommendations

Rejuvenation of stem cell activity might increase life expectancy by prolonging functionality of organs. Higher stem cell replication rates also bear the risk of cancer. The extent of this risk is not known. While it is difficult to evaluate this cancer risk in experiments, it can be estimated using a mathematical model for tissue homeostasis by stem cell replication and associated cancer risk. The model recapitulates the observation that treatments targeting stem cell replication can induce a substantial delay of organ failure. The model predicts that the cancer risk is minor under particular conditions. It depends on the assumed implications for cell damage repair during treatment. The benefit of rejuvenation therapy and its impact on cancer risk depend on the biological age at the time of treatment and on the overall cell turnover rate of the organs. Different organs have to be considered separately in the planning of systemic treatments. In recent years, the transfer of blood from young to old individuals was shown to bear the potential of rejuvenation of stem cell activity. In this context, the model predicts that the treatment schedule is critical for success and that schedules successful in animal experiments are not transferable to humans. Guidelines for successful protocols are proposed. The model presented here may be used as a guidance for the development of stem cell rejuvenation treatment protocols and the identification of critical parameters for cancer risk.

Monte Carlo economic analysis of Baker's yeast invertase purification using two‐ and three‐phase partitioning

AbstractBACKGROUNDInvertase use in the food industry is limited by production costs. Alternative strategies for extraction, such as aqueous two‐phase systems (ATPS) and three‐phase partitioning (TPP), could be economically feasible for yeast invertase. Economic modeling of bioprocesses makes possible the identification of critical parameters for production costs and emulation of real scenarios, moreover incorporation of uncertainty is possible. This study performed an economic analysis on the production of invertase using ATPS or TPP, also a virtual optimization of ATPS was done.RESULTSTPP provided a lower production cost than ATPS ($145 vs $59.3 per 1 million enzymatic units, respectively). The critical parameter for TPP is recovery yield as it is highly dependent on operating conditions. In contrast, ATPS is dependent on materials costs as the sample load is smaller for ATPS, requiring a larger system. Although TPP provided a lower cost, t‐butanol hinders its acceptance.CONCLUSIONVirtual optimization of ATPS found that varying sample load or system size is not enough to have a lower production cost than TPP, but provided insights for reduction of production costs and development of a safer technique. This study provides a framework for the virtual analysis of ATPS and TPP to evaluate their processes and reduce production costs. © 2018 Society of Chemical Industry

Simulation investigation of N2-injection enhanced gas drainage: Model development and identification of critical parameters

The performance of pre-gas drainage in low permeable seams especially in CO2 abundant coal seams is not satisfactory. Due to the high affinity of coal to CO2, much lead time is needed for gas drainage boreholes to reduce the coal seam gas content below the threshold limit value (TLV), thus meeting the demand of the scheduled mining activities. In this study, simulations of N2 injection enhanced gas drainage were carried out for CO2-rich coal seams. A binary gas migration model was developed to illustrate gas migration in coal seam. The simulation results matched well with the laboratory test results, indicating that this model could be used to investigate the mechanism of N2 flushing coal seam gas. The parameters affecting the flushing performance were then studied using this model, including coal seam permeability, gas diffusion coefficient, N2 injection pressure andin-situ gas content. These results suggest that coal seam permeability and CO2 diffusion coefficient are the key parameters having significant impacts on the drainage performance whilst N2 diffusion coefficient only has negligible effect. A critical parameter theory was developed to describe the correlation between these parameters (especially coal seam permeability and the diffusion coefficient of coal seam gas). Specifically, in low permeable coal seams, permeability was the critical parameter and the effects of other parameters were insignificant. Similarly, if the diffusion coefficient of coal seam gas was very low, it could become a critical parameter.

Application of game theoretic approaches for identification of critical parameters affecting power system small-disturbance stability

This paper evaluates a number of game theoretic (GT) approaches with the aim of assessing their suitability to identify the most influential parameters affecting the small-disturbance stability of a power system. Identification of the most influential parameters affecting system stability will facilitate cost-effective operation and control of a power system in general as it would require limited network monitoring, control and modelling effort by system operators and stakeholders. After identifying the most influential parameters the financial and human resources could be adequately prioritised and deployed to solve or prevent power system stability problems. A priority ranking procedure based on a GT approach has the advantage, compared to other techniques, of considering simultaneously the effect of individual and all possible combinatorial effects of the uncertain parameters (or players in a game context). In this study, the most influential players have been identified through a multi-level approach considering the power flow in the network (by optimal power flow), small disturbance stability aspects (by modal analysis), rational behavior of individual players (by a sensitivity technique) and formation of groups between players (by cooperative game theory). Various GT approaches have been considered namely Shapley Value, Aumann Shapley, Nucleolus, and τ-value approach in order to compare and assess their suitability for power system applications. The results are illustrated using two test networks and considering several approaches of sensitivity analysis and different degrees of variability in the considered uncertainties.

Estimation and sensitivity analysis of building energy demand

In modern societies buildings largely contribute to the energy demand and account in some countries for up to 45% of the primary energy consumption. Detailed consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain. In recent years, the intensified research effort by scientific communities and companies from the energy sector has led to an improved estimation of building energy consumption. This paper proposes an innovative tool that combines the estimation of the building energy consumption and supply costs with a sensitivity analysis. The estimation procedure considers both the building design and user interactions with the building. The effect of small variations in the building configuration on the building energy demand and supply costs is determined by the sensitivity analysis. The identification of critical parameters allows building managers to adopt appropriate measures to improve the energy efficiency of the considered building. For the demonstration and validation of the proposed approach, the tool is applied to a case of an office building located in Madrid (Spain). It is explained in detail how the estimates of the building energy consumption and the costs were calculated and the sensitivity analysis performed.

Identification of critical parameters for PEMFC stack performance characterization and control strategies for reliable and comparable stack benchmarking

This paper is focused on the identification of critical parameters and on the development of reliable methodologies to achieve comparable benchmark results. Possibilities for control sensor positioning and for parameter variation in sensitivity tests are discussed and recommended options for the control strategy are summarized. This ensures result comparability as well as stable test conditions. E.g., the stack temperature fluctuation is minimized to about 1 °C. The experiments demonstrate that reactants pressures differ up to 12 kPa if pressure control positions are varied, resulting in an average cell voltage deviation of 21 mV.Test parameters simulating different stack applications are summarized. The stack demonstrated comparable average cell voltage of 0.63 V for stationary and portable conditions. For automotive conditions, the voltage increased to 0.69 V, mainly caused by higher reactants pressures.A benchmarking concept is introduced using “steady-state” polarization curves. The occurring 20 mV hysteresis effect between the ascending and descending polarization curve can be corrected calculating the mean value of both voltages. This minimizes the influence of preceding load levels, current set points, and dwell times.

Neural network ensemble-based parameter sensitivity analysis in civil engineering systems

The use of artificial neural networks for parameter sensitivity analysis in civil engineering systems is an emerging research focus of increased interest. Existing methods are generally based on a single neural network, but are inadequate as a basis for parameter sensitivity analysis because of the instability of a single neural network. To address this deficiency, this study develops a neural network ensemble-based parameter sensitivity analysis paradigm. This paradigm features use of a set of preselected superior neural networks to make decisions about parameter sensitivity by synthesizing sensitivity analysis results of individual neural networks. The proposed paradigm is employed to address two classic civil engineering problems: (1) identification of critical parameters in the fracture failure of notched concrete beams and (2) recognition of the most significant parameters in the lateral deformation of deep foundation pits. The results show that tensile strength and modulus of elasticity are the critical parameters in the fracture failure of the notched concrete beam, and elasticity modulus of soil, Poisson’s ratio and soil cohesion are the most significant influential factors in the lateral deformation of the deep foundation pit. The proposed method provides a common paradigm for analysing the sensitivity of influential parameters, shedding light on the underlying mechanisms of civil engineering systems.

Estimation and Analysis of Building Energy Demand and Supply Costs

In modern societies buildings largely contribute to the energy demand and account in some countries for up to 45% of the primary energy consumption. Detailed consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain. In recent years, the intensified research effort by the scientific community and companies from the energy sector has led to an improved estimation of the building energy consumption. This paper proposes a tool that combines on one hand architectural characteristics and, on the other, user interactions with a building to precisely estimate its energy consumption and supply costs. The tool also determines the sensitivity of the energy consumption and the related costs for any variations applied in the building configuration or in the energy tariffs. The identification of critical parameters allows building managers to adopt appropriate measures to improve energy efficiency of the considered building. For the demonstration and validation of the proposed approach the developed tool is then applied to a case of an office building located in Madrid (Spain). It is explained in detail how the estimates of the building energy consumption and the costs were calculated and the sensitivity analysis performed.