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• Joint inspection and renewal planning for a system of multiple non-identical components. • Considering impact of preventive decommissioning of components that causes future performance loss of the system. • Optimal plan is competitive with condition-based maintenance approaches. • Case study of sugar production boiler to evaluate benefit of optimized strategy. In this paper, a system of multiple degrading components in series reliability is considered where the system performance is a monotonically increasing function of the number of working components. By decommissioning failed components, the system is restored to working order, but the performance loss may only be recovered when the entire system is renewed. The failure risks are managed by inspecting components and conducting preventive decommissioning based on the result. Thus, a complete maintenance and inspection policy will specify when and what components to inspect/decommission and when to renew the entire system. A joint inspection, decommissioning, and renewal plan is developed that minimizes the cost rate and this policy is applied to a boiler heat exchanger operating in a sugar mill. The results show that the proposed policy yields significant savings compared to a benchmark fixed-interval inspection policy and is competitive with a sophisticated condition-based approach. Moreover, in contrast to the condition-based benchmark, the proposed policy provides the entire inspection and renewal plan early in the system's life rather than re-planning after each inspection. Thus, the proposed approach is quite practical–it provides additional predictability in direct expenditures compared to the best condition-based policy and has statistically similar total costs. [ABSTRACT FROM AUTHOR]

For concentrating Solar Tower (ST) power plants, heliostats must be cleaned to maintain high productivity, but this comes at the cost of cleaning expenditures. Striking the correct balance remains challenging, due in part to the fact that soiling losses are location-dependent, stochastic, seasonal, and spatially inhomogeneous across the field. In this paper, novel reflectance-based cleaning policies are developed that trigger and prioritize cleaning of different solar field sectors based on reflectance measurements. In contrast to existing approaches, these policies have the potential to mitigate the effect of stochastic soiling losses and allocate finite cleaning resources by considering the spatial inhomogeneity of soiling. The optimization of the policy is conducted using the approximate Markov Decision Process (MDP) paradigm that utilizes a simulation model based on a recently developed physical soiling model. The proposed approach is applied to a case study on a hypothetical ST plant located in South Australia. The proposed policies are benchmarked with other traditional time-based cleaning policies and a previously developed reflectance-based policy. The results indicate a considerable benefit of sectorial reflectance-based cleaning strategies to other benchmarked policies (i.e. ∼ 2 % savings on total cleaning costs). Moreover, in case where the per-cleaning costs (e.g. water, fuel) are significant compared to the fixed costs (e.g. truck depreciation), the savings of proposed sectorial cleaning policies are greater (∼ 10% savings). • A reflectance-based sectorial cleaning policy for solar collectors is proposed. • Reflectance loss is modelled using a physical soiling model and weather parameters. • Cleaning policy is optimized using an Approximate Markov Decision Process paradigm. • The proposed policy is applied to a hypothetical plant in Woomera, South Australia. • Policies are benchmarked against other time-based and simplified cleaning policies. [ABSTRACT FROM AUTHOR]

Reflectance losses on solar mirrors due to soiling are a significant challenge for Concentrating Solar Power (CSP) plants. Soiling losses can vary significantly from site to site — with (absolute) reflectance losses varying from fractions of a percentage point up to several percentage points per day (pp/day), a fact that has motivated several studies in soiling predictive modelling. Yet, existing studies have so far neglected the characterization of statistical uncertainty in their parameters and predictions. In this paper, two reflectance loss models are proposed that model uncertainty: an extension of a previously developed physical model and a simplified model. A novel uncertainty characterization enables Maximum Likelihood Estimation techniques for parameter estimation for both models, and permits the estimation of parameter (and prediction) confidence intervals. The models are applied to data from ten soiling campaigns conducted at three Australian sites (Brisbane, Mount Isa, Wodonga). The simplified model produces high-quality predictions of soiling losses on novel data, while the semi-physical model performance is mixed. The statistical distributions of daily losses were estimated for different dust loadings. Under median conditions, the daily soiling losses for Brisbane, Mount Isa, and Wodonga are estimated as 0. 53 ± 0. 66 , 0. 08 ± 0. 08 , and 0. 58 ± 0. 15 pp/day, respectively. Yet, higher observed dust loadings can drive average losses as high as 2 pp/day. Overall, the results suggest a relatively simple approach characterizing the statistical distributions of soiling losses using airborne dust measurements and short reflectance monitoring campaigns. • New soiling loss models for CSP heliostats proposed that characterize uncertainty. • Statistical parameter estimation techniques enable parameter confidence intervals. • Models demonstrated on soiling campaigns from three locations in Australia. • Statistical distribution of soiling losses predicted under different dust loadings. • Results suggest a simple approach to predicting heliostat soiling losses. [ABSTRACT FROM AUTHOR]

Highlights • Multi-vibration data collected from slurry pump are analyzed with a novel method. • RUL are estimated from the suggested method with the help of Kalman Filter. • The results show that the suggested method is accurate in the two different cases. Abstract Slurry pumps handle abrasive and corrosive working fluids and their degradation rate can vary significantly depending on the composition of the slurry, making maintenance scheduling challenging. The paradigm of condition-based maintenance with accurately predicted remaining useful life (RUL) has the potential to significantly save on the total cost of maintenance. In this paper, a new methodology is presented for the RUL estimation for slurry pumps based on the fusion of data emitted from multiple vibration sensors, which enables the construction of a more reliable degradation index. Subsequently, the trend of the new degradation index is predicted using a Kalman Filter to estimate the parameters of a degradation trend line. Finally, an interval estimation of the RUL is obtained by analytically extrapolating the state space model to a pre-defined threshold. The proposed method is deployed to estimate the RUL for a slurry pump in a real production environment with multiple maintenance events, in contrast to previous studies which use limited, single run-to-failure data sets. The results show that the suggested method is capable to predict the RUL of the available datasets, even in the case where one channel is malfunctioning. [ABSTRACT FROM AUTHOR]

Highlights • A new degradation model for boiler heat exchanger erosion is developed. • The new model is a combination of a physics-based erosion model and a Gamma Process. • The new model is used to predict the total cost of maintenance for planning exchanger renewals. • A condition-based maintenance method is developed and demonstrated on a real boiler. Abstract Boiler heat exchanger tubes erode over time, leading to costly leaks and capacity loss as damaged tubes are taken out of operation. These losses can be recovered by replacing the heat exchanger, albeit at significant capital cost. Deciding when to replace the heat exchanger is greatly aided by prediction of the future capacity and downtime losses associated with tube thickness degradation. However, current thickness loss models study a single phenomenon and therefore provide oversimplified point predictions of thickness loss. Additionally, the available models do not account for uncertainty in the degradation process and therefore cannot provide information on future tube failure probabilities required for decision making. In this paper a novel degradation model is presented using a physical erosion model and a Gamma process to account for the uncertainties in the thickness degradation process. Subsequently, new algorithms are presented to predict the probability of key events in boiler operation: the bursting of a tube during operation and the plugging of a tube preventively during a scheduled shutdown. Finally, a condition-based maintenance methodology is presented based on the new degradation model and applied to a real-world case study of a boiler operating in a real Australian sugar production facility. [ABSTRACT FROM AUTHOR]

In building optimisation many parameters are uncertain due to their dependence on the building operation and environment (e.g. internal loads). This uncertainty implies that the “optimised” building is likely sub-optimal for the actual parameters. This study develops a new scenario-based optimisation methodology to address building parameter uncertainty. A multi-objective optimisation problem based on three objective functions (“low”, “base”, and “high” simulation scenarios) is developed and scalarised using the weighted sum method to find the optimised compromise between energy use for different scenarios. Necessitated by the increased computational demand of multi-objective problems, a modified version of the Ant Colony Optimisation algorithm for Mixed Variables (ACOMV-M) is developed. A comparison between ACOMV-M and a benchmark algorithm showed that ACOMV-M converged to solutions of similar quality with approximately 50% fewer simulations. The results on an Australian office building showed that the energy-optimised building parameters can vary significantly for different assumptions. Furthermore, inaccurate assumptions on internal loads and infiltration rate can reduce energy savings achieved by optimisation up to 4.8 percentage points. The proposed methodology is used to identify parameters that are sensitive to different scenarios and demonstrated that more robust solutions can be achieved through modest sacrifices in optimality to any one scenario. [ABSTRACT FROM AUTHOR]

In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations. [ABSTRACT FROM AUTHOR]

The search for optimal phase change materials (PCMs) for latent heat thermal energy storage systems (LHTESS) focuses almost exclusively on the properties of the PCM. This neglects the significant contribution of the cost of the containment vessel on the total cost of the LHTESS. Thus, to accurately assess the thermoeconomic performance of various PCMs, the relationship between the cost of the containment vessel and the properties of the PCM must be understood. This paper presents an analytical method for optimizing the design of tube and shell or tube and fin containment vessels for the least cost, subject to geometric and performance constraints. One of the key performance constraints is the time to charge, whose evaluation typically requires computationally-expensive simulations which are unsuitable for optimization algorithms. To enable efficient optimization, a novel closed-form approximation of the charge time is developed and validated through numerical simulations. Subsequently, the optimization methodology is used to investigate the relationship between the optimal vessel geometry and configuration, fin properties, PCM properties, and cost of two PCMs. For these PCMs, the use of high thermal conductivity fins is shown to dramatically reduce the total cost of the system. The new methodology is an efficient way to compare the thermoeconomic performance of PCMs as latent heat storage solutions, allowing for the accurate assessment of PCMs. [ABSTRACT FROM AUTHOR]

In this paper, a novel computationally-efficient optimization scheme is proposed to account for the effect of wind speed variations over non-flat terrain and irregular land plot boundary. In particular, typical analytical wake models are augmented with a wind multiplier (obtained from CFD simulations) to account for the effect of altitude variations, while irregular boundaries are addressed through novel constraint handling method in an unrestricted coordinate optimization formulation. In contrast to the optimization via CFD simulation alone, the augmented wake model provides acceptably accurate results with a reasonable computational burden. The new optimization approach is subsequently applied to two real wind farms with significant altitude variations: the Gokceada Island wind farm and the Grasmere and Albany wind farm. The optimized Gokceada wind farm layout exhibits a significant cost of energy (CoE) reduction when topography is considered (over conventional, flat-terrain optimization). However, the Grasmere and Albany wind farm shows a relatively small decrease in CoE over conventional optimization. This discrepancy can be attributed to the wind characteristics in each location: the Gokceada wind farm has a relatively consistent wind direction and the optimization can use topography to carefully avoid wake interactions. In contrast, the Grasmere and Albany wind farm with less-consistent wind directions leaves no clear method to avoid wakes using topography. These results indicate that the consideration of wind farm topography can yield significant benefits and the wake interactions can be greatly reduced through careful placement of wind turbines in the case where a narrow wind directions is dominant. [ABSTRACT FROM AUTHOR]

Reliability modelling requires accurate failure time of an asset. In real industrial cases, such data are often buried in different historical databases which were set up for purposes other than reliability modelling. In particular, two data sets are commonly available: work orders (WOs), which detail maintenance activities on the asset, and downtime data (DD), which details when the asset was taken offline. Each is incomplete from a failure perspective, where one wishes to know whether each downtime event was due to failure or scheduled activities. In this paper, a text mining approach is proposed to extract accurate failure time data from WOs and DD. A keyword dictionary is constructed using WO text descriptions and classifiers are constructed and applied to attribute each of the DD events to one of two classes: failure or nonfailure. The proposed method thus identifies downtime events whose descriptions are consistent with urgent unplanned WOs. The applicability of the methodology is demonstrated on maintenance data sets from an Australian electricity and sugar processing companies. Analysis of the text of the identified failure events seems to confirm the accurate identification of failures in DD. The results are expected to be immediately useful in improving the estimation of failure times (and thus the reliability models) for real-world assets. [ABSTRACT FROM AUTHOR]

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system. [ABSTRACT FROM AUTHOR]

Thermal fatigue cracking is a common problem for boiler waterwall tubes in power plants, resulting the risk of unscheduled plant shutdown. Developing an effective degradation model to predict the future condition of waterwall tubes is vital to assess boiler remaining life. Even though many numerical studies are available in the literature for understanding the physical mechanism (thermal fatigue cracking), the development of predictive models for future risks that can inform decision making are limited. One of the key impediments for developing predictive models is the lack of data. In the case of boiler critical subsystems, very few failures (if any) are recorded in its history. In addition, the number of inspections is limited due to access difficulties. Thus, in many practical cases, the condition data available for degradation modelling is sparse with both infrequent and (spatially) incomplete inspections. This paper presents the development of a stochastic degradation model prediction of thermal fatigue cracking severity in a boiler waterwall when the data is limited. The time evolution of the condition indicator for each waterwall tube is modelled using Markov Models and Bayesian identification algorithms are used to tractably address the large amount of missing data. The methodology presented in this paper employs a Markov Chain Monte Carlo (MCMC) scheme to account for the parameter uncertainty in the presence of missing data. Moreover, this paper also incorporates a novel grouping strategy in which the neighbouring components are grouped to tackle the problem of (spatial) data sparsity. The degradation modelling and prediction tools developed in this paper will support renewal decision making by using available onsite asset data and knowledge. A real-world case study of a boiler waterwall operating in an Australian power industry is also presented. It is found that the waterwall degradation appears to be slowing, but an additional inspection is recommended to confirm the trend. [ABSTRACT FROM AUTHOR]

In this article, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification, and monitoring methods are detailed that allow one to identify an HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment. [ABSTRACT FROM AUTHOR]

Today, the majority of semiconductor fabrication plants (fabs) conduct equipment preventive maintenance based on statistically-derived time-or wafer-count-based intervals. While these practices have had relative success in managing equipment availability and product yield, the cost, both in time and materials, remains high. Condition-based maintenance has been successfully adopted in several industries, where costs associated with equipment downtime range from potential loss of life to unacceptable affects to companies' bottom lines. In this paper, we present a method for the monitoring of complex systems in the presence of multiple operating regimes. In addition, the new representation of degradation processes will be used to define an optimization procedure that facilitates concurrent maintenance and operational decision-making in a manufacturing system. This decision-making procedure metaheuristically maximizes a customizable cost function that reflects the benefits of production uptime, and the losses incurred due to deficient quality and downtime. The new degradation monitoring method is illustrated through the monitoring of a deposition tool operating over a prolonged period of time in a major fab, while the operational decision-making is demonstrated using simulated operation of a generic cluster tool. [ABSTRACT FROM AUTHOR]

• Evaluate the effect of uncertain operating and market conditions on dispatch planning. • Apply Model Predictive Control (MPC) for dispatch scheduling of CSP plants. • Monte-Carlo simulation is to evaluate the MPC performance for a case-study in Australia. • The plant profitability from electricity sales under spot prices is assessed. The aim at dispatch scheduling for concentrating solar power plants is to utilize thermal storage to maximize profit from electricity generation. The dispatching plan, however, must contend with significant uncertainty in both weather forecasts (particularly solar irradiation) and, in some locations, real-time electricity prices governed by potentially volatile markets. This paper proposes a Model-Predictive Control (MPC) which employs a novel similarity-based forecast for weather variables and exploits the predictions of electricity prices provided by the market operator. A Monte-Carlo simulation is developed to evaluate true performance of the proposed MPC against two benchmarks, with perfect knowledge (PK) forecasts and day-ahead optimized using prototypical weather (DAPW). A case study is performed on a 115 MW Solar Tower plant with 8-hour storage, hypothetically located in South Australia, and operates under either a fixed-price or a real-time spot price scenario. A Monte-Carlo simulation is conducted for 150 tests for January (summer) and August (winter). The results show in fixed-price scenario MPC achieves 82.4 % of optimal profit (i.e. obtained via PK) in January and 72.4 % in August whereas that in DAPW falls to 71.5 % and 56.9 % of optimal, respectively. In the spot market scenario, MPC reaches 71.3 % and 63.6 % of optimal profit in January and August, whereas that for DAPW reaches 61.4 % and 55.1 %, respectively. In conclusion, PK forecast assumption in dispatch planning over-estimates the achievable profit by ∼ 28–40 % particularly in spot market scenario. Moreover, MPC can mitigate the influence of uncertainties on the plant economic performance by 8.5 %–15.5 % compared to DAPW benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors. [ABSTRACT FROM AUTHOR]

• Climate change and building load variations may impact optimised building designs. • A method using climate models and optimisation is used to optimise future buildings. • A case study is conducted on two cities in Australia: Canberra and Brisbane. • Results show that optimising for future climates can save energy for Canberra. • For Brisbane, savings are small and the present-optimised design seems acceptable. Building energy optimisation is generally performed under present climate conditions with fixed simulation parameters (e.g. internal loads). However, climate change and variations in simulation parameters over the building's life span may impact the optimised design. A key question is whether a particular energy-optimised design under present climate conditions would remain energy-optimised in the future. Accordingly, in this paper, a new simulation-based optimisation method is developed, which uses climate models and Ant Colony Optimisation to compare the energy-optimised designs under present and future climates. To demonstrate its potential, this method is applied to a typical office building in two Australian cities, Brisbane and Canberra. The results show that optimising under future climate conditions can lead to different optimal building designs. For Brisbane, the energy difference between optimising under present and future climate conditions is small, but in Canberra the cooling load is increased by up to 6%. This suggests that optimising the studied office building under present climate conditions is acceptable for Brisbane, while considering future climate may yield some savings in Canberra. Results also show that the energy-optimised building configuration for both future and present climates in Brisbane is less sensitive to changes in the load scenario than in Canberra. [ABSTRACT FROM AUTHOR]

As electricity prices and environmental awareness increase, more customers are becoming interested in installing distributed renewable generation, such as rooftop photovoltaic systems. Yearly load profile data could become very relevant to these customers to help them to time efficiently and accurately determine optimal energy investments for these customers. A new multi-dimension objective-oriented clustering-based method (MOC) is developed to identify a set of typical energy and/or demand periods. These typical periods can then be used to quantify the yearly cost savings for various renewable energy investment options. The optimal investment option can be determined after examining the financial viability of each option. This method was applied to a real community case study to evaluate renewable energy generation and storage options under two tariff situations: energy only or peak demand. Simulation results show that the MOC method can guide renewable energy investment planning with significant computational time reduction and high accuracy, compared to iterative simulations using a year of electricity load data. This energy investment planning method can help enable informed distributed renewable energy investment practices. • A clustering-based method to assess sustainable energy investment options. • A real site with combinations of PVs, batteries, different controls and tariffs. • Achieved 95% time saving and 97% or higher accuracy for the site case study. [ABSTRACT FROM AUTHOR]

One of the primary cost drivers for concentrated solar power operations and maintenance is the soiling of heliostats in the solar field. Although this loss can be largely recovered through cleaning operations, the associated costs are substantial and require careful consideration in relation to productivity gains. This paper presents a novel methodology for characterizing soiling losses through experimental measurements. Soiling predictions were obtained by calibrating a soiling model based on field measurements from a 50 MW modular solar tower project in Mount Isa, Australia. The study found that the mean predicted soiling rate for horizontally fixed mirrors was 0.12 pp/d during low dust seasons and 0.22 pp/d during high seasons. Autoregressive time series models were employed to extend two years of onsite meteorological measurements to a 10-year period, enabling the prediction of heliostat-field soiling rates. A fixed-frequency cleaning heuristic was applied to optimize the cleaning resources for various operational policies by balancing direct cleaning resource costs against the expected lost production, which was computed by averaging multiple simulated soiling loss trajectories. Analysis of resource usage showed that the cost of fuel and operator salaries contributed 42 % and 35 % respectively towards the cleaning cost. In addition, stowing heliostats in the horizontal position at night increased daily soiling rates by 114 % and the total cleaning costs by 51 % relative to vertically stowed heliostat-field. Under a simplified night-time-only power production configuration, the oversized solar field effectively charged the thermal storage during the day, despite reduced mirror reflectance due to soiling. These findings suggest that the plant can maintain efficient operation even with a reduced cleaning rate. Finally, it was observed that performing cleaning operations during the day led to a 7 % increase in the total cleaning cost compared to a night-time cleaning policy. This was primarily attributed to the need to park operational heliostats for cleaning. • Cleaning resources evaluated in the design phase of a modular solar tower power plant. • Soiling prediction methodology allows multiple soiling trajectories to be evaluated. • Parking heliostats horizontally at night reduces plant productivity. • CSP night-time power generation dispatch policies can tolerate higher soiling loads. • Cleaning during day reduces production for a negligible reflectance based production gain. [ABSTRACT FROM AUTHOR]

• Development of an optimized strategy for sectorial cleaning of solar fields. • Optical efficiency losses due to soiling based on a validated physical model. • The optimization of cleaning operations saves up to 20% of the related costs. • Dust concentration influences extremely the identification of the best strategy. • Mixed integer linear programming algorithm successfully applied for optimization. The reduction due to soiling of the optical efficiency of the heliostats in the solar field is a significant detrimental factor in concentrating solar power (CSP) plants. Artificial cleaning is required to maintain acceptable values of optical efficiency, especially in those areas where CSP tends to be economically viable, i.e. where the yearly available DNI is high and rain is scarce. The optimization of the cleaning activities is then a fundamental step to properly balance the operation and maintenance (O&M) costs of the plant with the revenue losses due to soiled heliostats. In this work the best cleaning schedule for a given solar field is computed through a mixed integer linear programming (MILP) model and compared with the results of a heuristic approach. The optical efficiency reduction is assessed for each sector of the solar field through a physical model. The MILP model accounts for the soiling impact and finds the most economical solution in terms of cleaning trucks number and number of cleanings. The optimal cleaning schedule for each sector of the solar field is obtained by minimizing the total cleaning cost (TCC), which is the sum of direct cleaning costs and monetized losses due to soiling. A few test cases are evaluated to demonstrate the strength and the applicability of the developed algorithm. The TCC improvements span between 0.7% and 19.6%, depending on the different scenarios and cost structures considered. For the case studies considered, the savings due to the MILP optimized cleaning strategy were between 927 kAU$/yr and 4744 kAU$/yr (575 k€/yr and 2941 k€/yr). [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated the economic benefit of exploiting partial opportunities for maintenance, which arise from the reduction of costs of doing partial maintenance utilizing the opportunity given. A typical example is an externally induced production stop with a duration that may not be sufficient to a complete maintenance activity. This paper combines partial opportunities and condition‐based maintenance (CBM) strategies and proposes an innovative maintenance optimization method considering time‐varying economic conditions. This scenario naturally fits a broad range of assets with a finite design life (including long‐life machinery and infrastructure), operating under variable economic conditions and usage‐intensities. The maintenance optimization problem is formulated in this study as a finite‐horizon Markov decision process, where the randomly occurring opportunities are accounted for by augmenting the time‐varying, decision‐dependent transition probabilities. A dynamic programming approach is subsequently used to obtain the optimal CBM policy, consisting of time‐varying thresholds on equipment condition and the cost of conducting maintenance during the arrived opportunity. A case study of such a maintenance policy for an induced draft fan of a sugar production system located in Queensland, Australia, is undertaken to evaluate the benefits of the proposed methodology against more traditional approaches that neglect opportunities or consider only replete opportunity durations. [ABSTRACT FROM AUTHOR]

The accumulation of soiling on photovoltaic modules and on the mirrors of concentrating solar power systems causes non-negligible energy losses with economic consequences. These challenges can be mitigated, or even prevented, through appropriate actions if the magnitude of soiling is known. Particle counting analysis is a common procedure to characterize soiling, as it can be easily performed on micrographs of glass coupons or solar devices that have been exposed to the environment. Particle counting does not, however, yield invariant results across institutions. The particle size distribution analysis is affected by the operator of the image analysis software and the methodology utilized. The results of a round-robin study are presented in this work to explore and elucidate the uncertainty related to particle counting and its effect on the characterization of the soiling of glass surfaces used in solar energy conversion systems. An international group of soiling experts analysed the same 8 micrographs using the same open-source ImageJ software package. The variation in the particle analyses results were investigated to identify specimen characteristics with the lowest coefficient of variation (CV) and the least uncertainty among the various operators. The mean particle diameter showed the lowest CV among the investigated characteristics, whereas the number of particles exhibited the largest CV. Additional parameters, such as the fractional area coverage by particles and parameters related to the distribution's shape yielded intermediate CV values. These results can provide insights on the magnitude inter-lab variability and uncertainty for optical and microscope-based soiling monitoring and characterization. • The precision of micrograph-based soiling characterization is analysed. • A group of experts conducted image analyses on the same soiling micrographs. • The results of image analysis are found to change depending on the operator. • Some variables (e.g. mean particle diameter) are more robust to image analysis. • ImageJ returned consistent results for multiple iterations of the employed methods. [ABSTRACT FROM AUTHOR]

Highlights • There is a need for safety to be included in eco-driving algorithms. • Eco-driving algorithms' efficiency reduces with unexpected traffic change. • We proposed a novel model predictive controller with hard constraint on safety. • The proposed method adapts in real-time to traffic. • Our model improves both safety and fuel efficiency. Abstract Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin's maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system. [ABSTRACT FROM AUTHOR]

The study of wind farm layout optimization considering the decisions of land owners has rarely been reported in literature. In this paper, the common situation of complex land divisions (e.g. unequally-spaced plots) is addressed for the first time. A new constraint handling and fitness evaluation technique is developed to address the more complex wind farm boundaries and integrated into two common wind farm optimization approaches: the grid based method and the unrestricted coordinate method. Enable by the new technique, a numerical optimization study is conducted with the goal of evaluating the impact of the participation of land owners on the economic performance of the wind farm. In particular, two scenarios are considered: 1) the varying land plot scenario, where the land plot availability is included in the decision variables of the optimization, and 2) the sequential land plot scenario, where the land plot availability is fixed prior to optimization. The study reveals that the unrestricted coordinate method under the sequential land plot scenario yields the best optimization results, with the smallest cost of energy and the largest wind farm efficiency. [ABSTRACT FROM AUTHOR]

• The operation of solar tower external receiver is dynamically simulated in Modelica. • The creep-fatigue lifetime of each receiver panel is estimated for three materials. • Temperature spikes due to clouds passages significantly affect the panels lifetime. • Creep-fatigue analysis based on clear-sky operation leads to 30% error on lifetime. This paper proposes a methodology for the dynamic thermal analysis of solar tower external receivers and for the assessment of their lifetime. A dynamic thermal model is implemented in Modelica to assess the temperature distributions of receiver tubes and heat transfer fluid (HTF), considering real weather data with a 10-minute time resolution and PI controllers based mass flow rate control. Three representative days are simulated, and the resulting tubes temperature distributions are used to assess the elastic stresses distributions during the investigated days. Subsequently, the creep-fatigue lifetime of each receiver panel is evaluated accounting for material plasticity and creep-induced stress relaxation. The developed methodology is applied to compare three materials for solar receivers: Inconel 740H, Haynes 230, and Incoloy 800H. Results show that fatigue can be negligible with respect to creep for all investigated materials and the shortest lifetime is obtained for 800H, followed by H230, and 740H. The approximation of a receiver operation for 365 clear-sky days per year leads to errors around 30 % on the lifetime. For 740H and 800H, the damage accumulation during cloudy days with high DNI peaks is greater than during clear-sky days due to the remarkable creep damage originated during temperature spikes which follow clouds passages. H230 instead, accumulates more creep damage during clear-sky operation. This emphasizes the potential of the developed methodology to compare different HTF mass flow rate control strategies as well as receiver geometries and materials, HTFs, and heliostats aiming strategies, accounting for the trade-off between energy yield and receiver lifetime. [ABSTRACT FROM AUTHOR]

• Assessing feasibility of inferring key track condition parameters using micro-electro-mechanical-system (MEMS) accelerometers. • Prototype of onboard data acquisition system designed and installed on a track recording car (TRC) and comparison of accelerometer-based results vs TRC recordings. • Accelerometers on the bogie are the best compromise between proximity to source and insensitivity to impulsive noise. • Two bogie accelerometers with two measurement axes can provide quantitative estimates of vertical alignment and trends in geographical distribution of horizontal alignment. • Frequency analysis of vertical/horizontal alignment formulas to justify choice of filtering parameters. The main objective of this paper is to assess the estimation of track condition parameters using onboard micro-electro-mechanical-system (MEMS) accelerometers. A prototype of an onboard data acquisition system was designed and installed on a track recording car (TRC) and a measurement campaign was conducted on an extensive portion of the Brisbane Suburban railway network. Comparison of the accelerometer-based results vs TRC recordings have shown that accelerometers installed on the bogie are the best compromise between proximity to the source and insensitivity to impulsive noise. It was found that two vertical bogie accelerometers (left and right) provide a good quantitative estimate of vertical alignment and that strong correlations with TRC measurements exist for lateral MEMS accelerometer measurements (horizontal alignment). These findings suggest that two bogie MEMS accelerometers with vertical and lateral measurement axes are effective in estimating geographical distributions of vertical/horizontal alignment. [ABSTRACT FROM AUTHOR]

The design optimization of external cylindrical receivers for solar tower plants is a complex task that involves several interrelated factors such as optical performance, thermal losses, pressure drops, mechanical integrity, and capital and operating costs. This work describes a comprehensive parametric methodology for the techno-economic optimization of external receivers design. The methodology is based on Levelized Cost of Heat minimization, relies on SolarPILOT for the optical analysis, and involves (i) three-dimensional steady state receiver thermal model, (ii) creep-fatigue lifetime assessment model, (iii) yearly performance assessment on hourly basis, and (iv) economic analysis. To ensure manageable computational efforts, some simplifying assumptions are introduced and successfully validated against more detailed approaches, thus allowing a more refined parametric analysis of design choices. The developed methodology is applied to optimize the receiver design of a Crescent Dunes-like plant comparing three different materials: Alloy 740H, Haynes 230, and Alloy 800H. Results show that the optimized peak heat flux is 1288 kW/m2 with 740H, 1245 kW/m2 with H230, and 868 kW/m2 with 800H. Moreover, the optimized receiver aspect ratio (height/diameter) is 1 for 740H, 1.25 for H230, and 1.5 for 800H. This highlights how the receiver material thermomechanical properties represent a crucial aspect for an accurate optimization of external receiver design. The developed methodology can be applied with any HTF, temperature range, solar field, tower height, and plant location. The flexibility of the proposed methodology, along with its limited computational demand, make it a powerful tool for the CSP industry and scientific research. [Display omitted] • A methodology is proposed to optimize the design of cylindrical solar receivers. • Optical and thermal performance, pressure drop, lifetime, and cost are considered. • The optimized aspect ratio for 740H, H230, and 800H is 1, 1.25, 1.5, respectively. • The optimized peak heat flux for the same materials is 1288, 1245, and 868 kW/m2. • The optimized design employs 740H with a corresponding LCOH of 20.8 €/MWh. [ABSTRACT FROM AUTHOR]

Ships require regular inspection and maintenance for corrosion defects that appear due to the failure of protective coatings in corrosive environments. However, maintenance planning is inhibited by the fact that coating failures (i.e. arrival of corrosion defects) are random and physical models for coating degradation and subsequent corrosion progression require data that is usually unavailable. In this paper, the maintenance history of frigates in the Royal Australian Navy is used to estimate the parameters of a Non-Homogeneous Poisson Process that describes the statistical properties of the coating failures in specific compartments of a vessel. A grouping heuristic is developed for fleet-wide data aggregation and parameter estimation. Finally, the predictions from these models are used to optimise the inspection and maintenance plan to simultaneously minimise the defect backlog and the unavailability of the ship due to repairs (since detailed cost data is unavailable). A case study is presented using data from a real fleet of eight ships and results show that 1) the grouping can slightly improve the generalization of the compartment models and 2) the trade-off between the defect backlog and the ship unavailability is clearly displayed as the set of Pareto-optimal solutions of the bi-objective optimisation. • NHPP models describe the appearance times of corrosion defects (coating failure) using real inspection records. • A grouping heuristic is developed for intelligent fleet-wide data aggregation and parameter estimation. • Optimisation for inspection plans developed to simultaneously minimise the defect backlog and ship unavailability. • Developed model is evaluated on a real case study. [ABSTRACT FROM AUTHOR]

[Display omitted] • CFS assessment is a complicated process that building designers tend to avoid using. • A simple CFS assessment framework is developed using a new tool CFStrace. • It introduces metrics to predict light uniformity ensuring adequate visual comfort. • The new framework generates fast daylight analysis results to select a suitable CFS. Complex fenestration systems (CFS) have proven to enhance the daylight quality inside spaces when designed correctly. While research in this area focuses on assessing CFS performance accurately, the number of CFS daylight analysis frameworks are too complex for the building designer to use for selecting a suitable CFS. Also, some CFS design frameworks focus only on light redirection efficiency without considering the spatial or environmental context. The paper demonstrates a new framework to analyse CFS considering both architectural and environmental conditions. The framework is presented as a computational tool (CFStrace), which accepts parametric geometric input and uses forward raytracing to simulate the complex optical behaviour of CFS. The framework also introduces new metrics for the assessment of CFS, which are developed to suit the forward raytracing simulation technique, that predicts the uniformity of redirected sunlight in the built environment and ensures adequate visual comfort to the occupants. A comparison study is carried out to state the effectiveness of the metrics where various CFS designs from previous literature are tested. The comparative case study revealed which CFS designs would be more suitable at the specific setting and location and provided insight on metrics that can be used to assess the feasibility of one design over the other in terms of daylight performance. The new framework will involve less technical and modelling capabilities making it "architect-friendly" encouraging building designers to include CFS in their design process, especially in the early design phase, and pave the way for new CFS designs possibilities. [ABSTRACT FROM AUTHOR]

• Maneuvers of mainline facilitating vehicles and on-ramp merging vehicles are coordinated. • Facilitating speeds are bounded from below to restrain impact on following traffic for safety. • The Pontryagin Maximum Principle is applied to rigorously derive analytical solutions. • The proposed strategy evaluated for mixed traffic through MATLAB-Aimsun interaction. • Sensitivity test conducted to check effects of different allowable minimum facilitating speeds. This paper presents a trajectory planning strategy for connected automated vehicles (CAVs) to cooperatively carry out mainline facilitating (i.e. gap development) and on-ramp merging maneuvers. The trajectory planning tasks of the mainline facilitating vehicle and the merging vehicle are formulated as two related optimal control problems. The motivation behind the proposed strategy is to restrain a facilitating maneuver's impact on following traffic. To this end, the proposed strategy bounds the speed of the facilitating maneuver from below and meanwhile ensures that the task of gap development can still be fulfilled. Because of the existence of the speed bound, the optimal control problem of the facilitating vehicle becomes constrained in state, in addition to the control constraints. A Pontryagin Maximum Principle (PMP) with state constraints is applied to rigorously derive the analytical solution. The main difficulty of the analytical procedure exists in the fact that the first-order necessary condition on the extremality of the Hamiltonian cannot yield useful information on the property of the optimal control history with regard to making the optimal speed trajectory to satisfy the speed constraint. As a result, additional conditions have to be explored, notably the so-called "jump conditions", among others. Taking advantage of the analytical solution, the proposed strategy is then implemented under a model predictive control framework. Simulation assessments of the proposed strategy are conducted at two levels – individual vehicle level and traffic flow level. At the individual vehicle level, the proposed strategy shows potential to reduce the risk of rear-end collision between the facilitating vehicle and the following vehicle, the most vulnerable pair of vehicles under the influence of gap development. At the traffic flow level, coupled with Aimsun, the proposed strategy is assessed under mixed traffic flow conditions, with various penetration rates of CAVs. The results show that it has potential to generate lower speed variations of traffic flow, a critical factor in traffic flow safety. Meanwhile it does not show negative impact on traffic efficiency in the simulation, and is likely to improve traffic efficiency in the real world. A sensitivity analysis of the effect of the facilitating maneuver's lower speed bound is also conducted. Although there exist several limitations with this study, it sheds some light on future research. [ABSTRACT FROM AUTHOR]

Concentrated Solar Power (CSP) receivers, especially those adopted in Solar Tower (ST) configurations, operate at high temperatures and low stresses making them susceptible to creep damage. Creep modelling used to predict components' life and therefore the accuracy of creep modelling can significantly impact life estimation, prediction of failures, and ultimately the design. The accuracy of creep modelling is highly variable and depends on the choice of model, the choice of fitting functions, and sometimes depends on 'judgement' decisions by the modeller. In this article the Larson-Miller and Wilshire creep models are fitted to three common power generation materials (Grade 22, SS316 and Inconel 740H) with differently sized datasets. Region splitting via activation energy is also examined. The fits are examined for accuracy and bias for stress, temperature and rupture time by a statistical process of bootstrapping to provide a better understanding of the impact of the creep model and dataset size on the design and lifing of CSP receivers. • Considers the impact of dataset size on creep model accuracy and biases. • Examines model accuracy w.r.t. rupture time, stress and temperature. • First paper to use boostrapping to examine the stability and biases of creep. • Traditional creep models and region splitting examined. • Provides authors with awareness of the impact of creep model selection. [ABSTRACT FROM AUTHOR]

In this paper, a real wind farm layout optimization is carried out by applying the novel optimized wind farm control strategy (coordinating wind turbine operations by setting different axial induction values) and multiple wind turbine hub height selections. The unrestricted coordinate method (using Cartesian coordinates to determine wind turbine positions) is applied to study the effectiveness of different wind farm control strategies and wind turbine hub heights on the wind farm optimization. Means of handling the irregular boundary constraints and calculating the wind speed over non-flat terrain at different heights are proposed for the real wind farm. The results show that the optimized control strategy can always yield better results than the self-optimum control strategy, achieving a smaller cost of energy production and a larger wind farm efficiency. Quantitatively, the optimized control strategy yields approximately 9 kW more power per turbine than the self-optimum control strategy and reduces the cost of energy by 0.08 million dollars per megawatt with 39 wind turbines installed. The application of different hub height turbines facilitates to place wind turbines more flexibly achieving better optimization results than that with constant hub height turbines. [ABSTRACT FROM AUTHOR]

Abstract The wind farm layout optimization is an effective tool to alleviate the wake power losses caused by wind turbine interactions in a wind farm. It is widely recognized that for a real wind farm site with abundant wind resources, wind speed variation of the wind condition can be approximated by the Weibull distribution, which therefore needs to be incorporated into the power evaluation of a real wind farm design. Current researchers have employed the discretization method by dividing the wind speed region into bins to calculate the power output of wind turbines under Weibull distribution, which has two main drawbacks including: 1) complicated discretization process with large computational cost and 2) dependency of calculation accuracy on the discretization resolution. This paper aims to propose a new Monte Carlo method to evaluate the wind farm power output under Weibull distribution, and verify the effectiveness and efficiency of the new method by comparison to the widely used discretization method. Through reducing the wind speed discretizing interval, it is found that when the discretized wind speed interval is smaller than 0.1 m/s, the improvement of optimization results is negligible while the computational cost significantly increases for the discretization method. By testing different sample numbers, it is found that selecting 100000 samples for the Monte Carlo method calculation is able to achieve accurate results with less computational cost than the discretization method especially when large number of turbines is installed in the wind farm. In conclusion, the Monte Carlo method greatly facilitates the power evaluation of wind farm layout optimization under Weibull distribution, with a balance between the calculation accuracy and the computational cost. Highlights • A novel Monte Carlo method for evaluating wind farm power under Weibull distribution is proposed. • Different wind speed discretizing intervals of discretization method for evaluating total power are tested. • Comparison of effectiveness of discretization method and Monte Carlo method is made. • When discretization is less than 0.1 m/s, improvement is negligible but cost significantly increases. • Monte Carlo method greatly facilitates wind farm layout optimization under Weibull distribution. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to systematically study the wind farm optimization with the continuous selection of wind turbine placement (using Cartesian coordinates) and wind turbine hub height (restrained among a predefined range). Two case studies are performed. The first case involves an ideal two-dimensional (i.e., flat terrain) wind farm, and the other is a three-dimensional wind farm with real terrain altitudes. The schemes of simultaneously optimizing the wind farm layout and wind turbine hub heights applying the simplified and augmented PARK wake model are established for the ideal and real wind farm cases, respectively. The results show that applying different wind turbine hub heights for the wind farm layout optimization yields significant improvement: up to a 0.2 MW increase in total power and a 2% increase of the wind farm efficiency for the ideal wind farm. However, for the real wind farm the effectiveness of applying different wind turbine hub heights varies depending on the number of turbines installed. With less number of turbines installed, the impact of varying hub heights is small. However, significant improvements can be achieved as the number of turbines increases. With 39 wind turbines, the wind farm cost of energy can be reduced by $15000 per megawatt and the wind farm efficiency can increase by up to 0.2%. Given the nameplate capacity and the lifespan of wind farm project, the resulting effect on total energy production can be significant and thus improve the competitiveness of wind power exploitation. Highlights • A combined optimization of wind turbine positions and hub heights is conducted with continuous variation. • A computationally-efficient method considering wake effect over non-flat terrain is applied. • Ideal wind farm yields 0.2 MW more power and 2% increase in efficiency with maximum hub height difference of 40 m. • Real wind farm reduces cost of energy by $15000 per megawatt with maximum hub height difference of 40 m. [ABSTRACT FROM AUTHOR]