In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial ...and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage.
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•BEXP and gamma models have been considered to investigate the trifluralin dissipation.•A simulation study illustrated the importance of the standard error of half-life.•Provided mathematical computation of the standard error of the half-life.•The soil moisture levels influenced the half-life estimates of trifluralin.
•Corrosion, discoloration, delamination and breakage are the main modes PV modules degradation.•Corrosion and discoloration are the predominant modes of silicon PV module degradation.•Temperature, ...humidity and UV radiation are the main factors of silicon PV module degradation.•Modeling of PV module degradation is still poorly studied in literature.•Accelerated tests are an alternative for investigating PV module degradation.
PV modules are often considered to be the most reliable component of a photovoltaic system. The alleged reliability has led to the long warranty period for modules up to 25years. Currently, failures resulting in module degradation are generally not considered because of the difficulty of measuring the power of a single module in a PV system and the lack of feedback on the various degradation modes of PV modules. It should be noted that consumers are becoming more and more interested in the reliability and lifetime of their PV system considering economic issues. Reliability and lifetime of a PV system depend mainly on the energy performance of modules and their different degradation modes. Accordingly, research must more and more focus on photovoltaic modules degradation. This paper presents a review of different types of degradation found in literature in recent years. Thus, according to literature, corrosion and discoloration of PV modules encapsulant are predominant degradation modes. Temperature and humidity are factors of PV modules degradation in almost all identified degradation modes. However, despite the identification of PV modules degradation modes, it is still difficult to study them in real conditions. Indeed, there must be long periods feedback experiences to study the frequency, speed of evolution and impacts of various PV modules degradation modes on energy output. In this paper, models associated with the PV modules degradation are presented. These models can help to overcome the long-term experiments obstacle in order to study PV modules degradation under real conditions.
Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance ...procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. Maintenance decisions identify optimal times to repair every component, which in turn, determine the failure risk of the turbines. More specifically, optimization models that characterize a turbine's failure time as the first time that one of its constituent components fail - a systems reliability concept called competing risk is developed. The resulting turbine failures impact the optimization of wind farm operations and revenue. Extensive experiments conducted for multiple wind farms with 300 wind turbines - 1200 components - showcases the performance of the proposed framework over conventional methods.
•A condition-based maintenance and operations model is proposed for wind farms.•Component and turbine dependencies on failure risks and maintenance are modeled.•A tailored solution algorithm is proposed to ensure computational scalability.•A comprehensive experimental framework is developed via degradation and wind data.•The proposed approach provides significant improvements over benchmark models.
This paper presents an experimental and analytical study about the mechanical response at elevated temperature of glass fibre reinforced polymer (GFRP) pultruded profiles. The paper first describes ...results of DMA and DSC tests that were used to evaluate the glass transition and decomposition processes of the GFRP pultruded material. The paper then describes an extensive study about the tensile, shear and compressive responses of the GFRP material at temperatures varying from 20°C to 250°C. In these tests the mechanical responses of the GFRP material as a function of temperature were assessed, namely the load–deflection curves, the stiffness, the failure modes and the ultimate strength. Results obtained in these experiments confirmed that the mechanical performance of GFRP is severely deteriorated at moderately high temperatures, particularly when loaded in shear and compression, owing to the glass transition of the resin. The final part of this paper assesses the accuracy of different empirical models and one phenomenological model to estimate the tensile, shear and compressive strengths of GFRP pultruded material as a function of temperature. All empirical models, including a function based on Gompertz statistical distribution suggested in the present paper, provided accurate estimates of tensile, shear and compressive strengths as a function of temperature. The phenomenological model was less accurate and in general provided non conservative estimates of material strength.
The maintenance strategy in railway transportation is crucial in ensuring safety, availability, and reducing operating costs. However, finding the optimal maintenance plan that takes into account the ...complex relationships between railway assets can be a challenging task. To address this challenge, this study introduces an Intelligent Petri Net (iPN) model to effectively consider the maintenance and operation of railway sections with a focus on optimizing ballast maintenance. The iPN model merges Petri net (PN) with Reinforcement Learning (RL) to create a model that is able to simulate and learn at the same time. The model is able to use diverse information, including usage, degradation rates, maintenance effectiveness, fault probabilities, and maintenance time, to simulate and learn at the same time. By considering the interconnections between these factors, the model found that reducing unnecessary maintenance actions increases the age of railway sections and leads to higher net profits. The study also introduced a method to reduce computational effort by dividing the PN into subnets and another method to make learning faster by using multiple RL environments. In conclusion, the developed iPN model presents a promising solution for optimizing ballast maintenance within railway operation.
•An Intelligent Petri net model was created to optimize railway maintenance and operation.•Proposed a Petri net decomposition method that cuts computational costs.•Presented a novel approach: Multiple Reinforcement learning environments for efficient optimization.•The maintenance policy was optimized to lower risks, reduce costs, and boost revenues.
Abstract
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and ...management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle–Fuller–Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.
The anaerobic batch digestion of freeze‐dried and ground (< 1.5 mm) maize silage, wheat straw, cattle manure, pig manure, and cellulose filter paper was investigated with substrates placed in ...polyester filter bags. Gas production dynamics of bagged and non‐bagged substrates were compared. By using batch bottles running in parallel with those used for gas measurement, substrates could be sampled over time and various parameters measured for both substrate and bulk liquid. The bags allowed an immediate mass loss into the bulk liquid with some substrates and a generally lower rate of gas production. This method requires refinement but has potential for the study of the dynamics of substrate degradation during anaerobic digestion.
The anaerobic degradation of a range of materials in a batch assay was performed with substrates in fiber bags to enable easy separation from the inoculum material after specific digestion periods. The technique allowed for temporal tracking of nitrogen and fiber degradation, although a lag phase in gas production was measured, when compared to the normal batch digestion method.
Battery management system (BMS) is an integral part of the Lithium-ion battery (LIB) for safe operation and power management. The advanced BMSs also provide state of charge (SOC) and state of health ...(SOH) information. Accurate estimation of the SOC and SOH from a sparse set of input and output measurements (voltage, current, and surface temperature) is challenging due to the internal inter-related complex electrochemical side reactions. Several factors, such as charge/discharge rate, operating temperature, internal aging, abnormal charging-discharging cycles, and internal faults, adversely affect the LIB's health. To aid the development of intelligent and robust BMS with the capability of health-conscious decision making, a deep understanding of the internal degradation mechanisms and the effect of external degradation-inducing factors are of primary importance. This paper presents an in-depth review of internal and external degradation mechanisms at both anode and cathode of LIB with their corresponding mathematical models and correlation with SOH metrics (capacity and power fade). Different electrochemical models integrated with the internal degradation mechanisms and their governing equations are discussed and summarized. The effects of the external aging factors on capacity and power fade and the dominant degradation mechanism under cycling and stored conditions are also reviewed and tabulated for quick reference. Recent developments in BMS's capabilities for SOH estimation using advanced and intelligent algorithms under various internal degradation conditions are also presented. Finally, the challenges in modeling, estimation of SOH, and several future research directions for developing self-learning and smart BMS are provided.
•Modeling studies on internal degradation mechanisms and their relation to SOH metrics.•Different electrochemical models integrated with the internal degradation mechanisms for commercially available graphite and metal anodes.•Individual and combined contributions of external aging factors to capacity and power fade•Advanced SOH estimation methods accounting for the influence of both internal and external aging factors•Discussion and future research directions for intelligent lithium-ion battery management systems
•Two approaches are presented for the RUL prediction of products with two-phrase degradation.•The RUL under gamma process without degradation rate change is considerably underestimated.•The RUL ...prediction for a specific product can be obtained, although the rate change has not occurred.•The SEM yields relatively less bias and more reliable interval estimates.•The Bayesian approach requires less computational time.
Remaining useful life prediction has been one of the important research topics in reliability engineering. For modern products, due to physical and chemical changes that take place with usage and with age, a significant degradation rate change usually exists. Degradation models that do not incorporate a change point may not accurately predict the remaining useful life of products with two-phase degradation. For this reason, we consider the degradation analysis for products with two-phase degradation under gamma processes. Incorporating a probability distribution of the time at which the degradation rate changes into the degradation model, the remaining useful life prediction for a single product can be obtained, even though the rate change has not occurred during the inspection. A Bayesian approach and a likelihood approach via stochastic expectation-maximization algorithm are proposed for the statistical inference of the remaining useful life. A simulation study is carried out to evaluate the performance of the developed methodologies to the remaining useful life prediction. Our results show that the likelihood approach yields relatively less bias and more reliable interval estimates, while the Bayesian approach requires less computational time. Finally, a real dataset on LEDs is presented to demonstrate an application of the proposed methodologies.
This study describes a GIS-based software tool for the qualitative assessment of desertification risk. The tool integrates results from several land degradation models into ArcGIS using the ...object-oriented programming language Visual Basic. The integration of GIS and models is based on different coupling approaches.
The proposed methodology was developed and tested on Sardinia Island (Italy). Six driving factors of desertification (overgrazing, vegetation productivity, soil fertility, water erosion, wind erosion and seawater intrusion) were model-simulated over two time periods to investigate the spatio-temporal evolution pattern of desertification-prone areas. Model results were normalized, weighted and combined into an Integrated Desertification Index (IDI) ranging from 0 to 1 (representing the best and the worst conditions, respectively), and classified into five desertification risk levels.
The implementation, performance of the methodology and benefits provided by the modelling approach to land management authorities for monitoring processes of land degradation are fully described in the paper.
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