Prediction Of Long-term Performance Research Articles

Thermodynamic Modelling for the Prediction and Optimisation of Long-Term Performance in Wooden Structures

Thermodynamic Modelling for the Prediction and Optimisation of Long-Term Performance in Wooden Structures

Long-term performance prediction of ground source heat pump system based on co-simulation and artificial neural network

Ground source heat pump (GSHP) systems are efficient and widely used form of shallow geothermal energy utilization. In practical application, GSHP systems face the problem in predicting the long-term performance and energy efficiency variations in GSHP systems in regions with thermal imbalances. In this study, a coupling heat transfer model of vertical ground heat exchanger (GHE), heat pump, and building load was established to achieve refined simulation calculations of the long-term operation of GSHP systems. Parametric modeling was conducted by employing the co-simulation of COMSOL and MATLAB, enabling dynamic simulations of 600 sets of GSHP system models throughout the entire year. A database was established by extracting the numerical results, and a prediction model was developed using artificial neural network (ANN) methodology to learn from the database. The model predicted the ground temperature, seasonal energy efficiency ratio (SEER), seasonal coefficient of performance (SCOP), annual electricity consumption, and annual energy savings of the GSHP system and the prediction model was valid. The results were used to estimate the ground temperature, coefficient of performance (COP), payback period for static investment, and carbon dioxide emission reduction. Finally, the prediction model was applied to a GSHP system engineering project in Yancheng, China, forecasting the operation performance of the system for 10 years. The accuracy of ANN in predicting 10 years of long-term performance is verified.

A Climate-Sensitive Transition Matrix Growth Model for Masson Pine (Pinus massoniana Lamb.) Natural Forests in Hunan Province, South-Central China

Masson pine natural forests are ecologically and economically valuable forest ecosystems extensively distributed across China. However, they have been subject to deforestation due to human disturbance. Moreover, climate change affects the growth, mortality, and recruitment of forests, yet available forest growth models do not effectively analyze the impacts of climate. A climate-sensitive transition matrix model (CM) was developed using data from 330 sample plots collected during the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventories in Hunan Province. To assess model robustness, two additional models were created using the same data: a non-climate-sensitive transition matrix model (NCM) and a fixed probability transition matrix model (FM). The models were compared using tenfold cross-validation and long-term predictive performance analysis. The cross-validation results did not show any significant differences among the three models, with the FM performing slightly better than the NCM. However, the application of the CM for long-term prediction (over a span of 100 years) under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) revealed distinct dynamics that demonstrated enhanced reliability. This is attributed to the consideration of climate variables that impact forest dynamics during long-term prediction periods. The CM model offers valuable guidance for the management of Masson pine natural forests within the context of changing climatic conditions.

A Hybrid Medium and Long-Term Relative Humidity Point and Interval Prediction Method for Intensive Poultry Farming

The accurate and reliable relative humidity (RH) prediction holds immense significance in effectively controlling the breeding cycle health and optimizing egg production performance in intensive poultry farming environments. However, current RH prediction research mainly focuses on short-term point predictions, which cannot meet the demand for accurate RH control in poultry houses in intensive farming. To compensate for this deficiency, a hybrid medium and long-term RH prediction model capable of precise point and interval prediction is proposed in this study. Firstly, the complexity of RH is reduced using a data denoising method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and permutation entropy. Secondly, important environmental factors are selected from feature correlation and change trends. Thirdly, based on the results of data denoising and feature selection, a BiGRU-Attention model incorporating an attention mechanism is established for medium and long-term RH point prediction. Finally, the Gaussian kernel density estimation (KDE-Gaussian) method is used to fit the point prediction error, and the RH prediction interval at different confidence levels is estimated. This method was applied to analyze the actual collection of waterfowl (Magang geese) environmental datasets from October 2022 to March 2023. The results indicate that the CEEMDAN-FS-BiGRU-Attention model proposed in this study has excellent medium and long-term point prediction performance. In comparison to LSTM, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are reduced by 57.7%, 48.2%, and 56.6%, respectively. Furthermore, at different confidence levels, the prediction interval formed by the KDE-Gaussian method is reliable and stable, which meets the need for accurate RH control in intensive farming environments.

Spatiotemporal Self-Attention-Based Network Traffic Prediction in IIoT

The sixth-generation (6G) mobile communications are considered as a future network and very closed to the Industrial Internet of Things (IIoT) due to its low latency and high throughput. Massive nodes supported by 6G make up the complexity of the network. Moreover, the heterogeneous traffic brings difficulties to the network management. Long-term network traffic matrix (TM) prediction is a crucial technology for realizing network edge intelligence and dealing with the above issues. However, predicting long-term network traffic in heterogeneous IIoT is challenging. Due to the powerful feature extraction capability over long sequences, self-attention is widely applied in language inference tasks. Motivated by these observations, we propose a self-attention traffic matrix prediction (SATMP) model for long-term network TM prediction in IIoT scenarios. SATMP consists of three components: (a) a spatial–temporal encoding for obtaining the spatial–temporal features of network TM; (b) a learnable positional encoding for providing positional correlation to the traffic sequence; and (c) a self-attention module for capturing long-term dependence. These components work together to enhance long-term prediction performance in complex networks effectively. Extensive experiments on three publicly available datasets demonstrate that SATMP is feasible and accurate in IIoT long-term network TM prediction.

Parameter modelling of fleet gas turbine engines using gated recurrent neural networks

Oriented by the gas-path fault diagnosis of aircraft engines, this work presents a novel parameter modelling scheme for fleet gas turbine engines based on gated recurrent neural networks. Four dynamic models are constructed based on the long short-term memory (LSTM) network and gated recurrent unit (GRU) network to predict the total temperature at the high-pressure turbine outlet of gas turbine engines. We apply the dynamic networks to model the engine parameters by solving a sequence-to-feature regression problem. The proposed scheme is assessed through a comprehensive comparison study on training performance, validation performance, generalization, robustness against noise, and extrapolation performance. The results showed that the proposed network models performed a promising performance on parameter modelling. For the proposed four network models, the NARX-based models have better one-step-ahead prediction performance, while the state-space-based models have better long-term prediction performance. The discussion of the results also showed that the GRU network was an effective data-driven model for parameter modelling of gas turbine engines.

Seismic degradation behavior of steel-FRP composite bar reinforced concrete beams in marine environment based on experimental and numerical investigation

This study investigated the seismic behavior of seawater sea sand concrete (SWSSC, which is made of seawater, sea sand, ordinary aggregate and cement) beams reinforced with steel bars in a marine environment. To improve the durability of the specimens, different types of reinforcements, namely fiber reinforced polymer (FRP), steel-FRP composite bars (SFCBs), and macro basalt fibers, were introduced to replace steel bars owing to their superior durability and high strength. The failure mode, hysteretic response, energy dissipation capacity, and curvature of the pedestal were analyzed. Furthermore, a long-term performance prediction method based on finite element (FE) modeling was proposed. Results showed that the bond between the longitudinal reinforcements and SWSSC in steel and SFCB reinforced concrete (RC) beams degraded significantly after corrosion. Additionally, slips were observed during reciprocating loading. The total cumulative dissipated energy of the SFCB RC beam was approximately the same as that of the steel RC beam. Moreover, the corresponding value of SFCB and macro basalt fiber hybrid reinforced concrete specimen was almost twice that of the steel RC beam owing to a stable confinement of fibers on concrete. After corrosion, steel and SFCB RC beams exhibited a decrease in the lateral displacement at the top and a larger curvature at the pedestal, indicating that the rotation of the corroded specimens mainly occurred at the beam-column joint. The accuracy of the FE model was verified by comparing the obtained results with the experimental results. The prediction results revealed that the seismic performance of SFCB RC beams in the marine environment declined in the first three years and then stabilized gradually.which is made of seawater, sea sand, ordinary stone and cement.

Individual hematotoxicity prediction of further chemotherapy cycles by dynamic mathematical models in patients with gastrointestinal tumors

PurposeHematotoxicity is a common side-effect of cytotoxic gastrointestinal (GI) cancer therapies. An unsolved problem is to predict the individual risk therefore to decide on treatment adaptions. We applied an established biomathematical prediction model and primarily evaluated its predictive value in patients undergoing chemotherapy for GI cancers in curative intent.MethodsIn a prospective, observational multicenter study on patients with gastro-esophageal or pancreatic cancer (n = 28) receiving myelosuppressive adjuvant or neoadjuvant chemotherapy (FLO(T) or FOLFIRINOX), individual model parameters were learned based on patients’ observed laboratory values during the first chemotherapy cycle and further external data resources. Grades of hematotoxicity of subsequent cycles were predicted by model simulation and compared with observed data.ResultsThe most common high-grade hematological toxicity was neutropenia [19/28 patients (68%)]. For the FLO(T) regimen, individual grades of thrombocytopenia and leukopenia could be well predicted for cycles 2–4, as well as grades of neutropenia for cycle 2. Prediction accuracy for neutropenia in the third and fourth cycle differed by one toxicity grade on average. For the FOLFIRINOX-regimen, thrombocytopenia predictions showed a maximum deviation of one toxicity grade up to the end of therapy (8 cycles). Deviations of predictions were less than one degree on average up to cycle 4 for neutropenia, and up to cycle 6 for leukopenia.ConclusionThe biomathematical model showed excellent short-term and decent long-term prediction performance for all relevant hematological side effects associated with FLO(T)/FOLFIRINOX. Clinical utility of this precision-medicine approach needs to be further investigated in a larger cohort.

External validation of bleeding risk models for the prediction of long-term bleeding risk in patients with established cardiovascular disease

ObjectiveThe long-term predictive performance of existing bleeding risk models in patients with various manifestations of cardiovascular disease (CVD) is not well known. This study aims to assess and compare the performance of relevant existing bleeding risk models in estimating the long-term risk of major bleeding in a cohort of patients with established CVD. MethodsSeven existing bleeding risk models (PRECISE-DAPT, DAPT, Ducrocq et al, de Vries et al, S2TOP-BLEED, Intracranial B2LEED3S and HAS-BLED) were identified and externally validated in 7,249 patients with established CVD included in the Utrecht Cardiovascular Cohort–second manifestations of arterial disease study. Predictive performance was assessed in terms of discrimination and calibration, both at 10 years and the original prediction horizon of the models. Major bleeding was defined as Bleeding Academic Research Consortium type 3 or 5. ResultsAfter a median follow-up of 8.4 years (interquartile range 4.5-12.5), a total of 233 (3.2%) major bleeding events occurred. C-statistics for discrimination at 10 years ranged from 0.53 (95%CI 0.49-0.57) to 0.64 (95%CI 0.60-0.68). Calibration plots after recalibration to 10 years showed best agreement between predicted and observed bleeding risk for De Vries et al, S2TOP-BLEED, DAPT and PRECISE-DAPT. ConclusionsThe performance of existing bleeding risk models to predict long-term bleeding in patients with CVD varied. Discrimination and calibration were best for the models of de Vries et al, S2TOP-BLEED, DAPT and PRECISE-DAPT. Of these, recalibrated models requiring the least predictors may be preferred for use to personalize prevention with antithrombotic therapy.

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

Reliability of AMBT and CPT in testing the effectiveness of SCM to mitigate alkali–silica reaction of field concrete

This paper examines the reliability of the Australian Standards accelerated mortar bar test (AMBT) and concrete prism test (CPT), AS 1141.60.1 and 60.2, respectively, and the potential for extending these standard test methods to the determination of the type and dosage of supplementary cementitious materials (SCMs) required to mitigate deleterious alkali-silica reaction(ASR) in a similar manner to the corresponding ASTMC1567 and CSAA23.2-28A. Both AMBT and CPT have their strengths and limitations, however, their overwhelming value is in the availability of laboratory test results and their correlation with long-term performance (up to 20 years) of large concrete blocks and structural elements. Since Australia has no existing field exposure site, this paper uses reported data from CANMET’s and Ontario Hydro’s outdoor exposure sites in Ottawa and Kingston, respectively, in Canada and the 18-year exposure site data from the BRE site in the UK to demonstrate the efficacy of the AMBT and CPT approaches to evaluation of ASR reactivity and ASR mitigation using SCM blended cements. Good correlations are observed for moderately–reactive and highly reactive aggregates used with low-alkali cement and cement with alkali contents up to 0.9 % Na2Oeq. The assessment of reported research demonstrates that both AMBT and CPT have the capacity to predict the long–term performance of structural concrete. In addition, AMBT and 2-year CPT test results for eight Australian reactive aggregates are reported showing the influence of SCMs (fly ash and slag) on mitigation of expansion. Whilst both AMBT and CPT demonstrated their potential for assessing SCMs in mitigation of ASR, AMBT was found to be more conservative than CPT and higher SCM dosages were required to achieve a non-reactive classification. The accelerated test data reported coupled with the correlation of AMBT and CPT to the prediction of long term performance of concretes strongly supports the extension of the Australian test methods AS1141.60.1 and 60.2 for the assessment of ASR mitigation using SCMs.

Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination

Membrane Capacitive Deionization (MCDI) is a promising electrochemical technique for water desalination. Previous studies have confirrmed the effectiveness of MCDI in removing contaminants from brackish groundwaters, especially in remote areas where electricity is scarce. However, as with other water treatment technologies, performance deterioration of the MCDI system still occurs, hindering the stability of long-term operation. Herein, a machine learning (ML) modelling framework and various ML models were developed to (i) investigate the performance deterioration due particularly to insufficient charging/discharging of the electrode caused by accumulation of ions and electrode scaling and (ii) optimise MCDI operating parameters such that the impacts of these deleterious effects on unit performance were minimized. The ML models developed in this work exhibited a prediction accuracy of cycle time with average mean absolute percentage error (MAPE) values of 16.82% and 16.09% after 30-fold cross validation for Random Forest (RF) and Multilayer Perceptron (MLP) models respectively. The pre-trained ML model predicted different declining trends of water production for two different operating conditions and provided corresponding recommendations on frequencies of chemical cleaning. A case study on the adjustment of operating parameters using the results suggested by the optimization ML model was conducted. The model validation results showed that the overall water production and water recovery of the system using the cycle-based optimized process control parameters (SCN 1) exceeds the MCDI system performance under three fixed parameter settings that were used at each stage of SCN 1 by 1.78% to 4.48% and 2.95% to 9.46%, respectively. Permutation-based and Shapley additive explanation (SHAP) coefficients were also employed for variable importance (VIMP) analysis to uncover the “black-box” nature of the ML models and to better understand the various features’ contributions to overall MCDI system performance.

Long-Term Water Quality Prediction Using Integrated Water Quality Indices and Advanced Deep Learning Models: A Case Study of Chaohu Lake, China, 2019–2022

The rapid development of urban industrialization has had many negative effects on the quality of water sources around cities. Long-term prediction of water quality can be of great help to the conservation of water environment. This case tries to use several popular deep learning models, such as RNN, LSTM, MLP, and Transformer-based models to predict the long-term integrated water quality index in the Chaohu Lake area. The dataset is derived from daily monitoring data from four monitoring sites within Chaohu Lake from 2019 to 2022, and the long-term prediction performance of the model is evaluated using MAE and MSE as evaluation metrics. The experimental results showed that all models selected in this case achieved good results within the study area, but Informer performed more prominently (MSE = 0.2455, MAE = 0.2449) as the length of the prediction series increased. Our results demonstrate the effectiveness of popular deep learning models in the field of WQI prediction, especially the significant advantage of transformer-based models represented by Informer in long-term water quality prediction, which will further provide an effective modern tool for water quality monitoring and management.

Construction of geofoam block embankments atop existing transportation infrastructure

Expanded polystyrene (EPS) block (geofoam block) is a mature geotechnology for constructing highway embankments. In addition to preventing bearing capacity and settlement related stability problems for the embankments constructed on soft soil sites, geofoam blocks are also used to construct embankments atop existing infrastructure. Selecting geofoam technology for these applications not only prevents possible structural problems due to the lightweight nature of geofoam blocks but also eliminates costly remediation alternatives. The details of three well-documented and monitored different geofoam embankments constructed atop different existing infrastructures (buried pipeline, buried culvert and pile cap of drilled shafts for a deep excavation) are presented. The backgrounds of these projects, design considerations, construction and instrumentation details are discussed. The monitoring results were presented, and long-term performance predictions were evaluated. Numerical modeling effort was utilized to model the both short- and long-term behavior of geofoam embankments. Time-dependent behavior of the embankments under service loads was compared with numerical simulations. Constitutive modeling and related mechanical properties of geofoam blocks, which mimicked the long-term field behavior, as a function of geofoam density were proposed for future numerical modeling efforts.

Durability and Long-Term Performance Prediction of Carbon Fiber Reinforced Polymer Laminates.

The feasibility of strengthening deteriorated or under-capacity concrete structures with external carbon-fiber-reinforced polymer (CFRP) laminates has been widely validated in the literature. However, there is a lack of knowledge on the in situ long-term performance and age-related environmental degradation of the mechanical properties of the laminates. The current study involved the immersion of coupons from a common new CFRP laminate in heated water at 23, 45, and 60 °C for 224 days. The coupons were then tested for residual tensile properties, such as tensile capacity and elastic modulus, using ASTM D3039 (2017) specifications. The CFRP tensile capacity and elastic modulus decreased by a maximum of 33% and 26%, respectively, for 224 days of exposure. Based on the test data, an age-based long-term prediction model with excellent reliability for CFRP laminate tensile capacity was developed. The model was then calibrated with test results from old CFRP coupons collected from an existing CFRP laminate retrofitted concrete bridge. The calibrated model output was then compared with the environmental reduction factor from ACI 440.2R-17 and a few other common sources. It was found that the ACI specified a reduction factor of 0.75, which does not consider the CFRP age and overestimates the design tensile strength of the CFRP laminate by approximately 13%, which may compromise the structural safety of retrofitted bridges. The reduction factors from the other guidelines varied between 0.51 and 0.85.

Multiscale simulation of temperature- and pressure-dependent nonlinear dynamics of PMMA/CNT composite plates

Owing to the excellent mechanical properties, polymethyl methacrylate (PMMA)/carbon nanotube (CNT) composite has been increasingly adopted in the aerospace field, which is usually subjected to various temperature conditions and supersonic aerodynamic loads. Using a molecular dynamics (MD)-based multiscale simulation, the nonlinear forced vibration of PMMA/CNT composite plate is investigated under coupled temperature and aerodynamic load conditions. The longitudinal, transverse and shear moduli of PMMA/single-walled CNT (SWCNT) nanocomposite obtained from MD simulation at different temperature and pressure levels are substituted into the extended rule of mixtures to establish the constitutive equations of PMMA/CNT composite plate. Meanwhile, Poisson’s ratio and thermal expansion coefficient of nanocomposite are used in the constitutive equations. Based on the third-order shear deformation theory, von Karman nonlinear strain–displacement relation and Hamilton’s principle, the partial differential equation of composite plate is derived, which is reduced into a set of coupled ordinary differential equations by applying Galerkin method and is solved using the fourth-order Runge–Kutta method. The bifurcation diagrams, phase portraits, time histories and Poincare maps of composite plate are obtained under the complex loads including transverse harmonic excitation and aerodynamic pressure, which are applied to analyze the dynamic characteristics of system. This study reveals the nonlinear dynamic characteristics of PMMA/CNT composite plate, which contributes to the prediction of long-term performance of composite materials in aerospace field.

A fusion prognostics strategy for fuel cells operating under dynamic conditions

Transportation-oriented proton exchange membrane fuel cells (PEMFC) are attracting much attention, but the strong dynamic operating conditions in transportation applications limit the durability improvement and wide commercialization of fuel cells (FC). Prognostics dedicated to predicting the FC remaining useful life (RUL) can facilitate the early provision of control/maintenance programs to improve durability and reduce costs. However, credible degradation indexes for prognostics are difficult to access or observe directly from the FC operating under dynamic conditions. Moreover, the long-term prediction performance of the state-of-the-art prognostics models is often not satisfactory. This paper proposes a fusion prognostics strategy to address these challenges. Specifically, the system dynamics is identified by using an electrochemical mechanism model and the degradation indexes are extracted based on the identified model parameters. Subsequently, a reduced-dimensional symbolic representation based long short-term memory network is developed for predicting the evolution of degradation. The proposed approach is validated using the long-term accelerated stress test data of a vehicle-oriented PEMFC. The results show that the degradation mechanism model can be used to identify degradation indexes in dynamic operating conditions. Based on the prognostics model, accurate RUL prediction can further be achieved over the extracted degradation indexes.

Gridding Optimization for Hydraulic Fractured Well in Reservoir Simulation Using Well Test Analysis for Long Term Prediction

Analytic models, complex simulations, and simple models are being used to predict the production performance of hydraulically fractured shale. Analytical models such as decline curve analysis and rate transient analysis are used for a quick evaluation of reservoir performance. However, they have considerable limitations. For instance, decline curve analysis cannot honor the physical phenomena in shale wells that are related to hydraulic fracture, reservoir characteristics, and fluid flow. On the other hand, even though explicit hydraulic fracture modeling is the most comprehensive approach when compared with other traditional techniques, it cannot guarantee to model enough hydraulic fracture effects. Hence, calibration of the model, which commonly is referred to in the oil and gas industry as history matching, becomes a must. However, history matching of an explicit hydraulic fracture model with limited information is time-consuming and cumbersome. Especially history matching of a full field shale gas/oil model with many wells is a daunting task. In this study, we propose a workflow to integrate numerical reservoir simulation and well test analysis. In the workflow, information such as fracture half-length and enhanced effective permeability are obtained from pressure transient analysis and are used to calibrate grid properties in the vicinity of the plane covered by the fracture length and width. Finally, the simulation model is calibrated using pressure and flow rate data, and it is used for the long-term performance prediction of a hydraulic fractured well. The workflow was evaluated by using a synthetic reservoir model whose permeability mimicked that of a shale formation. As a result, the workflow thus enabled the use of coarse grid blocks, which, in turn, reduced the simulation time to just 1.5% of the simulation runtime consumed by a reference fine grid model.

A comprehensive evaluation of process kinetics: A plant-wide approach for nutrient removal and biogas production

The present study investigated the deviations of operational parameters of a large-scale wastewater treatment plant (WWTP) from design basis through combining dedicated batch experiments with full-scale dynamic modeling results. The long-term process performance of a full-scale biological nutrient removal (BNR) plant equipped with anaerobic sludge digestion system was monitored to evaluate the process kinetics of both carbon and nutrient removal and anaerobic sludge digestion. In this respect, plant-specific characterization; chemical oxygen demand (COD) fractionation, batch kinetic studies and sludge settling velocity tests were performed together with plant-wide SUMO model simulation. Results showed that nitrification and anaerobic hydrolysis were found to be 30% and 70% lower than literature values, respectively. The anaerobic digestion test coupled with plant-wide model calibration showed that anaerobic hydrolysis was the bottleneck in biogas production. Correspondingly, performance of the anaerobic digestion in the full-scale plant was poor as low biogas production yields were observed. In addition, the degradation rate via anaerobic hydrolysis of primary sludge was found to be higher (∼2-2.5) compared to anaerobic hydrolysis of biological sludge. The results of this study provide insight into model-based experimental characterization as well as plant-wide modeling approach. Coupling model-based batch experiments with full-scale modeling enabled to reduce the number of kinetic parameters to be fine-tuned. Moreover, the information gathered from kinetic batch tests to the simulation platform yielded a satisfying prediction of long-term performance of the plant operation.

A data-driven method for state of health prediction of lithium-ion batteries in a unified framework

State of health (SOH) is paramount performance for the management and maintenance of lithium-ion batteries. SOH prediction is of great significance to state of charge (SOC) estimation and remaining useful life (RUL) prediction. To achieve accurate SOH prediction in a unified framework including one-step, multi-step and long-term prediction, empirical mode decomposition (EMD) is introduced to diminish the local fluctuation, and then the decoupled residual SOH series is regarded as the training series. The optimized dynamic single-exponential model is used to describe the SOH degradation. Subsequently, the optimal system state is determined by particle filter (PF) algorithm. Effectiveness and dominant of this method are validated via the multiple simulation comparative experiments based on National Aeronautics and Space Administration (NASA) data set. Additionally, one-step, multi-step and long-term SOH prediction performance are analyzed in detail. The results indicate that the proposed method realizes a unified prediction framework with uncertainty representation for lithium-ion batteries and outperforms other important methods with higher prediction accuracy. Furthermore, the prediction results are still considerable even with a small number of historical SOH data. Note that this method is also employed in other similar battery management systems.