Simulation Case Research Articles

SAL Method Applied in Grid Forecasting Product Verification with Three-Source Fusion Product

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.

EV Charging Prediction in Residential Area Based on SE‐GRU‐MA Model Consider Multi‐Source Data Feature Mining

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large‐scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE‐GRU‐MA residential EV charging load prediction method that incorporates multi‐source data feature mining. The proposed method employs a multi‐source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE‐GRU‐MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE‐GRU‐MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A bi-objective optimization framework for configuration of battery energy storage system considering energy loss and economy

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Three-Dimensional CKANs: UUV Noncooperative Target State Estimation Approach Based on 3D Convolutional Kolmogorov–Arnold Networks

Accurate and stable estimation of the position and trajectory of noncooperative targets is crucial for the safe navigation and operation of sonar-equipped underwater unmanned vehicles (UUVs). However, the uncertainty associated with sonar observations and the unpredictability of noncooperative target movements often undermine the stability of traditional Bayesian methods. This paper presents an innovative approach for noncooperative target state estimation utilizing 3D Convolutional Kolmogorov–Arnold Networks (3DCKANs). By establishing a non-Markovian model that characterizes state estimation of UUV noncooperative targets under uncertain observations, we leverage historical data to construct 3D Convolutional Kolmogorov–Arnold Networks. This network learns the patterns of sonar observations and target state transitions from a substantial offline dataset, allowing it to approximate the posterior probability distribution derived from past observations effectively. Additionally, a sliding window technique is integrated into the convolutional neural network to enhance the estimator’s fault tolerance with respect to observation data in both temporal and spatial dimensions, particularly when posterior probabilities are unknown. The incorporation of the Kolmogorov–Arnold representation within the convolutional layers enhances the network’s capacity for nonlinear expression and adaptability in processing spatial information. Finally, we present statistical experiments and simulation cases to validate the accuracy and stability of the proposed method.

Modern methods of diagnostics of syncopal conditions in servicemen

The review article is devoted to modern methods of diagnosing syncope, which is becoming more and more common among young people every year. This article discusses the causes and mechanisms of syncope, as well as methods of diagnosing and differentiating it. Particular attention is paid to syncope that occurs as a result of orthostatic hypotension. Unfortunately, syncope has long been ignored by both doctors and patients. It is not uncommon for patients to ignore isolated episodes of loss of consciousness, explaining them by lifestyle features. A long absence of any diagnosis, and, as a consequence, treatment leads to the progression of the underlying disease that provokes this syncope. But the problem of diagnosing syncope depends not only on patients, but also on doctors. Due to the lack of a clear algorithm that allows for accurate diagnosis of syncope and differentiation, doctors’ diagnostic capabilities are limited. However, this is not the only problem, since the lack of special equipment in most medical institutions also affects the quality of syncope diagnostics. Tilt test is currently the gold standard in syncope diagnostics according to the 2018 European Society of Cardiology recommendations. The equipment is expensive, and the diagnostic ability of this method does not provide the desired results for diagnosis verification. Today, the attitude to this problem is changing and in 2023, the development of a plan of clinical recommendations of the Russian Society of Cardiology began, where syncope is highlighted in a separate chapter. Accurate diagnostics of syncope is needed not only to determine the underlying disease. In the clinical practice of a doctor, there are often cases of simulation of syncope by young people of draft age for personal purposes. It is practically impossible to clinically confirm the presence of syncope in the past at the moment (especially if it was a single episode of loss of consciousness). The lack of a clear algorithm significantly complicates the verification of the diagnosis. The purpose of writing this article was to study the most effective diagnostic methods that allow you to most accurately determine the cause of syncope.

The effect of data-driving and relaxation models on magnetic flux rope evolution and stability

Understanding the flux rope eruptivity and effects of data driving in modelling solar eruptions is crucial for correctly applying different models and interpreting their results. We aim to investigate these by analysing the fully data-driven modelled eruption of the active regions (ARs) 12473 and AR11176, as well as preforming relaxation runs for AR12473 (found to be eruptive) where the driving is switched off systematically at different time steps. We intend to analyse the behaviour and evolution of fundamental quantities that are essential for understanding the eruptivity of magnetic flux ropes (MFRs). The data-driven simulations were carried out with the time-dependent magnetofrictional model (TMFM) for AR12473 and AR11176. For the relaxation runs, we employed the magnetofrictional method (MFM) and a zero-beta magnetohydrodynamic (MHD) model to investigate how significant the differences between the two relaxation procedures are when started from the same initial conditions. In total, 22 simulations were studied. To determine the eruptivity of the MFRs, we calculated and analysed characteristic geometric properties such as the cross-section, MFR height, and physical stability parameters such as MFR twist and the decay index. Furthermore, for the eruptive cases, we investigated the effect of sustained driving beyond the point of eruptivity on the MFR properties and evolution. We find that the fully driven AR12473 MFR is eruptive, while the AR11176 MFR is not. For the relaxation runs, we find that the MFM MFRs are eruptive when the driving is stopped around the flare time or later, while the MHD MFRs show eruptive behaviour even if the driving is switched off one and a half days before the flare occurs. We also find that characteristic MFR properties can vary greatly even for the eruptive cases of different relaxation simulations. The results suggest that data driving can significantly influence the evolution of the eruption, with differences appearing even when the relaxation time is set to later stages of the simulation when the MFRs have already entered an eruptive phase. Moreover, the relaxation model affects the results significantly, as highlighted by the differences between the MFM and MHD MFRs, showing that eruptivity in MHD does not directly translate to eruptivity in the MFM, despite the same initial conditions. Finally, if the exact critical values of instability parameters are unknown, tracking the evolution of typical MFR properties can be a powerful tool for determining MFR eruptivity.

A New Filter Based on Sliding Mode Method with Discrete‐Time Implementation for Noise Attenuation and Differentiation

AbstractThis paper presents a novel filter based on the sliding mode method for filtering noise and extracting reliable signals from noisy signals by enhancing Levant's sliding mode filter. Specifically, the proposed sliding mode filter takes advantage of the generalized signum function to adjust the gain in different regions of phase space, thereby decreasing the overshoot during the response phase. Additionally, the discrete‐time implementation of the proposed sliding mode filter is achieved by utilizing the implicit‐Euler algorithm, which effectively eliminates chattering in the output. The effectiveness of the presented sliding mode filter is substantiated via numerical simulation cases.

Deep convolutional sparse dictionary learning for bearing fault diagnosis under variable speed condition

Dictionary learning is a usual method in the field of machinery fault diagnosis, but it requires that the rotating speed conditions of training set and test set are the same and constant. When the speed condition of test set is different from that of training set or one of them is time-vary, normal dictionary learning is difficult to get a precise sparse representation. A special dictionary model named convolutional sparse dictionary (CSD) can overcome the influence from variable speed conditions by atoms locally shifting in the sample's dimension, which is beneficial to capture the local fault features in the signal no matter how the speed changes. However, there are both large features and small features in the mechanical vibration signal, and several continuous small features can also form a large feature. The problem is that CSD can only locally optimize the signal at a fixed scale, so the features of other scales cannot be optimized. To solve this problem, this paper proposes a model named deep convolutional sparse dictionary (DCSD) to extract bearing fault features under variable speed conditions, which is improved from CSD. DCSD has multiple dictionary layers, where each layer is a CSD, but the atom's dimensions are different in each layer. The larger the number of layer is, the larger the atom's dimension is, and the sparse representation result of each layer is used to train the next dictionary layer. Through simulations cases and experimental cases under variable speed conditions, it is proved that DCSD has better performances than CSD in the fault diagnosis.

Local signal detection on irregular domains with generalized varying coefficient models

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate nonparametric varying coefficient functions and impose a local penalty on L 2 norms of spline coefficients for each triangle to identify null regions of zero effects. Moreover, we develop model confidence regions as the inference tool to quantify the uncertainty of the estimated null regions. Our method partitions the region of interest using triangulation and efficiently approximates irregular domains. In addition, we propose an efficient algorithm to obtain the proposed estimator using the local quadratic approximation. We also establish the consistency of estimated nonparametric coefficient functions and the estimated null regions. The numerical performance of the proposed method is evaluated in both simulation cases and real data analysis.

PP180 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: EVALUATION OF RESIDENTS’ MECHANICAL VENTILATION MANAGEMENT BEHAVIORS IN A SIMULATED ONLINE PEDIATRIC ACUTE RESPIRATORY DISTRESS SYNDROME CASE SCENARIO

PP180 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: EVALUATION OF RESIDENTS’ MECHANICAL VENTILATION MANAGEMENT BEHAVIORS IN A SIMULATED ONLINE PEDIATRIC ACUTE RESPIRATORY DISTRESS SYNDROME CASE SCENARIO

Practice Patterns in Asymmetric Sensorineural Hearing Loss: Survey Data.

Although screening protocols for patients who present with asymmetric sensorineural hearing loss (ASNHL) exist, there are no clear guidelines to direct practitioners. In particular, various thresholds have been proposed for the degree of hearing loss that should prompt MRI studies, but the topic remains understudied. This project aims to compare protocols followed by practitioners to guide their imaging practices. Web-based survey. Otolaryngology faculty at academic medical centers. A list of 530 otolaryngologists (276 otology/neurotology specialists, 254 general otolaryngologists) was compiled. A survey consisting of three parts: demographics, general practice patterns, and simulated patient cases was distributed. A total of 468 surveys were successfully distributed, resulting in 88 (18.8%) responses. The majority of respondents (63.8%) self-reported their definition of ASNHL as ">30 dB hearing asymmetry at one frequency OR >20 dB hearing asymmetry at two continuous frequencies OR >10 dB hearing asymmetry at three contiguous frequencies." Overall, general otolaryngologists were more likely to observe asymmetric findings with serial audiogram alone, whereas otology/neurotology specialists were more likely to obtain imaging. There is significant variability between providers with regard to managing patients with ASNHL and evidence-based guidelines would be useful in guiding imaging practices. NA Laryngoscope, 134:4745-4753, 2024.

DeepPipe: A Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network for Hydraulic Transient Simulation of Multi-Product Pipeline

In the chemical pipelining industry, owing to the high-pressure transportation process, an accurate hydraulic transient simulation tool plays a central role in preventing the slack line flow and overpressure from causing pipeline operation treacherous. Nevertheless, the current model-driven method often faces challenges in balancing computational efficiency with accuracy, and the existing data-driven models struggle to produce explainable results from the physics perspectives since insufficient theoretical principles are incorporated into the model training. Additionally, the existing physics-informed learning architecture fails to achieve a gradient-balanced training, resulting from the significant magnitude difference in outputs and multiple loss terms. Consequently, a Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network (MS-KE-PINN) is proposed for the hydraulic transient simulation of multi-product pipelines. To enforce the neural network producing simulation results with high consistency to physical laws, the governing equations, boundary, and initial condition are incorporated into the training process for an efficient mesh-free simulation. Then, considering that the significant magnitude difference between outputs can easily lead to deficient performance in the gradient descent, the magnitude conversion on the outputs and the equivalent conversion of the governing equations are implemented to enhance the training effect of the neural network. Subsequently, to tackle the imbalanced gradient of multiple loss terms with fixed weights, a multi-stage hierarchical training strategy is designed to improve the approximation capacity of the neural network. Numerical simulation cases demonstrate a better approximation function of the proposed model than the state-of-art models, while the mean absolute percentage errors yielded by MS-KE-PINN are reduced by 77.4 %, 88.7 %, and 87.8 % in three simulation operation conditions for pressure prediction. Furthermore, experimental investigations from a real-world multi-product pipeline suggest that the proposed model can still draw accurate simulation results even under complex and dynamic hydraulic transient scenarios in practice, with root mean squared errors reduced by 94.8 % and 80 % than that of the physics-informed neural network. To this end, the proposed model can conduct accurate and effective hydraulic transient analysis, thus ensuring the safe operation of the pipeline.

Polymer Flooding Reduces Emissions and Energy Consumption in the North Sea

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218231, How Polymer Flooding Reduces CO2 Emissions and Energy Consumption at the Captain Field: An Exergy-Return-on-Exergy-Investment Case Study,” by Tormod Skauge, SPE, and Arne Skauge, SPE, Energy Research Norway, and Nancy Lugo, Ithaca Energy UK, et al. The paper has not been peer reviewed. _ Insights into the energy efficiency and environmental performance of the extraction of heavy oil from the Captain field in the UK sector of the North Sea are gained using life-cycle assessment (LCA), which incorporates the concept of exergy-return-on-exergy-investment (ERoEI). The LCA allows for separation of the process into material and work streams, assessing the effect of each separately. In the complete paper, the authors compare waterflooding (WF) to polymer flooding (PF) as primary extraction strategies. Introduction The Captain field is approximately 145 km northeast of Aberdeen, Scotland. The reservoirs lie at shallow depths of approximately 900 m true vertical depth with correspondingly low temperatures of 31°C and low pressures of 87 bar. A primary challenge is the relatively viscous oil of 40–140 cp. The field was initially developed with long horizontal wells and downhole pumps. Viscous oil implies mobility challenges, however, and water injection could lead to unstable displacement, early water breakthrough, and possible coning issues. After a successful pilot PF at Captain during 2010–2017, a staged enhanced oil recovery (EOR) development approach was adopted in 2016 aiming to extend the pilot polymer-solution-injection scheme to the full field through a phased development. Following the pilot study, the Captain EOR project was split into Stage 1 and Stage 2. The first stage involved drilling of five producers and two EOR injectors by 2022. The second stage included some brownfield modifications across the three Captain installations (two installations and a floating production, storage, and offloading vessel) and a significant expansion in the subsea area. The complete paper concentrates on the expansion in the subsea areas, Areas B and C. The methodology of the study, including the development of simulation cases, LCA, exergy analysis, system boundaries, material and work streams, and CO2 emissions, is detailed in the complete paper. Production Profiles The Stage 2 EOR development evaluates PF for Areas B and C. The baseline is WF with fully produced water reinjection. WF was scheduled to begin in January 2024 and to end in December 2099. The PF case also was slated to begin in January 2024 and to end in 2035 and 2034 for Area B and Area C, respectively. The field is operated on pressure maintenance, and the voidage replacement ratio is kept close to 1.0. This means that the PF injects more liquid than the WF case during the first 5 years of injection when the oil production is higher for PF. After polymer injection ends, the PF case injects less liquid than the WF case. In Area B, water cut is high from the start for the WF case. Because of the high mobility of the water, it fingers through the oil. In contrast, when polymer is injected, the water cut falls to approximately 80% and does not reach the starting value of 94.1% before 7 years, more than 2.5 years after having stopped the polymer injection in this area. The low water cut is the result of production of a large oil bank generated by the improved sweep and mobility reduction of the injected polymer.

Dependence of divertor asymmetries on the toroidal magnetic field

A study of the effect of toroidal magnetic field (Bt) on the divertor asymmetries is carried out with a plasma transport code under BOUT++ framework with the magnetic equilibria from EAST and C-Mod. For the simulation cases with drifts, the density is larger at the inner divertor target, while the temperature and heat flux are larger at the outer divertor target. The ratio of the total particle flux at the outer target to that of the inner target increases with increasing Bt, while the ratio of the total heat flux at the outer target to that of the inner target decreases with increasing Bt. The in–out divertor asymmetries of both total particle and heat fluxes get weaker with increasing Bt. Further analysis shows that the edge radial transport induced by drifts is much weaker for the simulation case with higher Bt, indicating that drift-driven divertor asymmetries may be less important for future tokamaks with high Bt.

Pediatric critical care nursing empowerment through distance education and simulation in Accra, Ghana

BackgroundThe study assessed a distance simulation and education intervention designed to improve nursing empowerment in the pediatric intensive care unit (PICU) in Korle Bu Teaching Hospital (KBTH) in Ghana. MethodsThis was a single center, mixed methods, longitudinal simulation-based pilot study. A distance pediatric critical care nursing curriculum that combined interactive learning via Zoom and distance simulation using the Virtual Resus Room was designed and delivered from the United States to KBTH PICU nursing staff. ResultsThe Clinical Teamwork Scale, used to assess team performance after each simulation, demonstrated an upward trend over time in all categories. The modified Simulation Effectiveness Tool was utilized to evaluate the learners' perceptions of the effectiveness of the simulation in meeting their learning objectives. Qualitative content analysis of this free-text feedback highlighted the course's educational content, organizational aspects, learning experience, simulation case scenarios and relevance to pediatric critical care in Ghana. ConclusionsA distance pediatric critical care nursing curriculum, relevant to local practice and resources, was implemented through an international partnership and contributed to nursing empowerment through improved learning and confidence.

Two-phase regularized phase-field density gradient Navier–Stokes based flow model: Tuning for microfluidic and digital core applications

Here we present a regularized phase-field Navier–Stokes two-phase flow model with density gradient theory for interface treatment. The usage of regularization allows us for faster computations — for some particular simulation cases the time step could be increased x9 times. The computations are stable and spurious layers are suppressed with the help of nonlinear surface energy model. To verify the model we test rigorously against major classic problems: (1) droplet on a flat wall with given contact angle, (2) analytical solution for capillary rise, (3) hydrodynamical focusing within a micro-channel, (4) displacement in a pore doublet. As the application example, we simulate two-phase flow displacement within an X-ray microtomography scan of oil-bearing rock and demonstrate computation of relative permeabilities. We believe that developed model will be useful in numerous research applications: porous media design with desired physical properties, digital core technology applications to obtain flow properties of rocks and enhance hydrocarbon recovery, hydrological applications to study unsaturated flow in soils. Finally, we discuss potential improvements of the model as related to computational efficiency.

Research on Frequency Regulation with Dynamic Trajectory Planning of Participation Factors

The energy transformation has led to a high proportion of new energy entering the grid. New energy has high randomness, which challenges the frequency stability of the power system. A new approach to improve frequency based on dynamic trajectory planning of participation factors is proposed in this work. In the frequency regulation interval, the trajectory and termination time of participation factors are regarded as the variables to be optimized. Then, taking the frequency regulation performance and economy as the goals, a multi-objective optimization problem is constructed. The participation factors are used to dispatch the output power of different units, which breaks the constraint that the output characteristics of different units are consistent in the original regulation process. Therefore, the generator units with different regulation characteristics in the system achieve complementary coordination on the second time scale. In simulation cases, frequency oscillation is reduced, while the overall economy of the system is improved. The numerical evaluation results show that the frequency performance is improved by 2.7% at most and 2.9% of the economy is improved at most simultaneously. The above results demonstrate the effectiveness of the proposed approach.

Tafoni and laboratory‐based experiments of historical contingent phenomenon

AbstractControlled experiments on natural forms involve a traditional view of explanation that prioritizes control, manipulation and replication over more nuanced understanding of the phenomena under investigation. This is an issue if the phenomenon, like tafoni, is historically contingent. In particular, the entanglement of the environment is explicitly severed in controlled experiments. The failure to adequately reproduce the ordered forms associated with tafoni in laboratory experiments suggests that simulation and field case studies could provide a more informative route to understanding tafoni. This will, however, require a more systematic recognition of how such research enables researchers to understand the initial conditions of weathering and modes of development to tafoni observed.

Nursing Students’ Satisfaction with Clinical Simulation: A Cross-Sectional Observational Study

Clinical Simulation improves results in the students’ learning tests and allows for preserving acquired knowledge for longer periods of time, promoting more significant learning. This study was conducted to analyze Nursing students’ satisfaction with Clinical Simulation in three centres attached to a university from southern Spain. Methods: A quantitative, non-experimental and cross-sectional descriptive study was carried out. The students included were attending their third year of the Nursing undergraduate course and had already taken part in training sessions by means of Clinical Simulation. The Satisfaction Scale with High-Fidelity Clinical Simulation in Students (SSHF) was used for data collection. This scale has been validated and has 33 items grouped into eight factors. The SPSS software (version 28), was used for data analysis, establishing p-values < 0.05 for the statistically significant differences. Results: The participants were 180 students, with a mean age of 22.17 years old. Of them, 90.56% belonged to the female gender. A mean score of 3.82 out of 5 was obtained in the SSHF items. The items that obtained the highest scores were the following: benefits of Clinical Simulation as it relates theory with practise; possibility of learning based on the mistakes made; and comfort and respect while the sessions were developed. The item that obtained the lowest score was “timing for each simulation case”. We found significant differences in the results obtained according to each attached centre. Conclusions: The students showed high satisfaction levels regarding High-Fidelity Clinical Simulation in each of the three attached centres included in the study. Nevertheless, they stated the need to invest more time in Clinical Simulation sessions.

Integrating Process Simulation and Life Cycle Assessment for Enhanced Process Efficiency and Reduced Environmental Impact in Ferromanganese Production

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO2 emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO2 emissions.