Validation Of Simulation Model Research Articles

Video Context Improves Performance in Identifying Operative Planes on Static Surgical Images

Correct identification of the surgical tissue planes of dissection is paramount at the operating room, and the needed skills seem to be improved with realistic dynamic models rather than mere still images. The objective is to assess the role of adding video prequels to still images taken from operations on the precision and accuracy of tissue plane identification using a validated simulation model, considering various levels of surgeons' experience. A prospective observational study was conducted involving 15 surgeons distributed to three equal groups, including a consultant group [C], a senior group [S], and a junior group [J]. Subjects were asked to identify and draw ideal tissue planes in 20 images selected at suitable operative moments of identification before and after showing a 10- second videoclip preceding the still image. A validated comparative metric (using a modified Hausdorff distance [%Hdu] for object matching) was used to measure the distance between lines. A precision analysis was carried out based on the difference in %Hdu between lines drawn before and after watching the videos, and between-group comparisons were analyzed using a one-way analysis of variance (ANOVA). The analysis of accuracy was done on the difference in %Hdu between lines drawn by the subjects and the ideal lines provided by an expert panel. The impact of videos on accuracy was assessed using a repeated-measures ANOVA. The C group showed the highest preciseness as compared to the S and J groups (mean Hdu 9.17±11.86 versus 12.1±15.5 and 20.0±18.32, respectively, p <0.001) and significant differences between groups were found in 14 images (70%). Considering the expert panel as a reference, the interaction between time and experience level was significant ( F (2, 597) = 4.52, p <0.001). Although the subjects of the J group were significantly less accurate than other surgeons, only this group showed significant improvements in mean %Hdu values after watching the lead-in videos ( F (1, 597) = 6.04, p = 0.014). Adding video context improved the ability of junior trainees to identify tissue planes of dissection. A realistic model is recommended considering experience-based differences in precision in training programs.

Effects of adding polystyrene blocks in rectangular containers on radio frequency heating rate and uniformity of watermelon seeds

Non-uniform heating is a main problem for applying radio frequency (RF) treatments to eliminate the seed-borne pathogens in seeds before commercial import/export. In this study, thermal and dielectric properties of watermelon seed coats and embryos with different moisture contents were measured for computer simulation. The simulation model of RF heated watermelon seeds with or without adding a polystyrene block was developed and validated based on experimental results using the commercial COMSOL software. The effect of adding different sizes of polystyrene block on RF heating rate and uniformity of watermelon seeds was also studied by the validated simulation model. The results showed that the difference in heating rate between first and second layers and average temperature of seeds increased from 0.63 to 18.78 °C/min and 40.74 to 58.60 °C with increasing thickness and height of polystyrene block increased from 5 to 30 mm and 4 to 28 mm. The volumetric RF heating uniformity index in seeds decreased with decreasing heating rate difference. Meanwhile, the height of 7.7 mm combined with thickness of 25 mm of the polystyrene block could provide the best RF heating uniformity for watermelon seeds. These findings from the established new methods to improve the heating uniformity could be used to develop an effective RF treatment protocol for controlling the seed-borne pathogens of watermelon seeds without reducing their quality.

Combining econometric analysis and simulation modeling to evaluate Population-Based health policies for chronic disease prevention and control

ObjectivesPopulation-based health policies play an important role in preventing and controlling chronic disease. Policymakers need to understand both the short- and long-term impacts of different policies to optimize resource allocation. The objective of this study is to develop a framework that combines econometric analysis and simulation modeling for a comprehensive evaluation of population-based health policies. Study designBoth econometric analysis and simulation modeling were used to evaluate the impact of a population-based health policy. MethodsWe identified a cohort of hypertensive patients from the 2011–2013 China Health and Retirement Longitudinal Study and fitted the data into our framework to evaluate the effectiveness of a community-based hypertension-screening program under the Essential Public Health Services (EPHS) policy on the future burden of cardiovascular disease in China. ResultsUsing an econometric approach, we identified that the community-based hypertension screening program would lead to a 7.9% improvement in the rate of hypertension control. Using a validated simulation model, we further estimated that if the policy was fully implemented nationwide, it could avert 97,100 cases of myocardial infarction and 215,600 cases of stroke. The policy would cost $2131 on average to save 1 quality-adjusted life year over 10 years. ConclusionsThis study proposed a framework integrating two different methods and assessing both short- and long-term impact of a population-based health policy. Through a case study, we demonstrated that combining econometric analysis and simulation modeling could provide policymakers with a more powerful tool to evaluate health policies for controlling chronic disease.

Developing a hybrid data-fitting procedure and a case study for patient service time

ABSTRACT The validity of input data plays a significant role in achieving valid output for simulating modeling systems. This study developed a systematic procedure to deal with the stochastic input data that cannot lead to a good probabilistic distribution using traditional data-fitting techniques. The proposed procedures used hybrid data-fitting techniques, including traditional data fitting, changes to the number of classes of a histogram, F3 distribution, and empirical distribution, in order to obtain a corresponding probabilistic distribution for those ill-fitting input data. Employing one year of historical data from the case image center, this research focused on the service time of computed tomography (CT) outpatients, classifying the CT outpatients into six categories based on their scan type. The results showed that the proposed data-fitting procedure could determine an appropriate probabilistic distribution for the stochastic scan time for each CT scan type. Therefore, the proposed data-fitting procedure could help decision makers construct a validated simulation model in order to evaluate different alternatives and determine a better one in the future.

Research on simulation model validation based on probability relational analysis

The most basic and direct method for simulation model validation is to compare the consistency of missile flight data and simulation data under the same input conditions. However, the existing dynamic data consistency analysis methods are mainly suitable for the case between 1-D missile flight data and 1-D simulation data, and do not conform to the consistency test of single sample flight data and multi-sample simulation data in equipment qualification/finalization test. To solve this problem, a simulation model validation method based on probabilistic relational analysis is proposed. The consistency of output data is measured from the two scales of probability relational coefficient and probability relational degree. The probability relational coefficient is determined by calculating the cumulative distribution probability value of real missile flight samples in the distribution function constructed by simulation data. The probability correlation degree is calculated by judging whether the probability relational coefficient satisfies the uniform distribution of[0 1]. The consistency analysis problem of a kind of dynamic data association is solved accordingly. The correlation theorem that the probability relational degree must satisfy and its property are proved. Meanwhile the operation steps of simulation model verification based on probability correlation analysis are given. This method can process all multi-dimensional simulation data at the same time, and integrate the random factors in the test process, so it can make full use of the test information under the condition of small sample flight test, and improve precision and the reliability of simulation model verification. The rationality and validity of this method are further verified by numerical tests and application examples.

Deterministic modeling of solidification microstructure formation in directed energy deposition fabricated Ti6Al4V

Metal additive manufacturing (MAM) technology is now widely applied in the manufacturing and remanufacturing industry, among which microstructure simulation gradually shows its importance. Traditional solidification microstructure simulation methods all have their merits as well as drawbacks when applied in MAM cases. In this work, a deterministic solidification microstructure model, named “invasion model”, is established to avoid the essential defects of traditional methods. This model focuses on the interaction between the neighboring bi-crystals instead of simulating the growth kinetics of each columnar grain or deriving the field form of variables. Within a bi-crystal system, the grain boundary tilt from thermal gradient direction is understood as a transient invasion behavior of one grain to another, and the competitive grain growth behavior along the buildup process of MAM is a summary of all the invasions in bi-crystal systems. To fill the gaps in the rapid solidification theory, a database recording the anisotropic growth effect under a rapid directional solidification condition was established with the help of an artificial neural network (ANN). Wire feed directed energy deposition (DED) fabricated Ti6Al4V thin-wall samples with full dendritic columnar grains (prior beta) are used as a benchmark to test the validation of the novel simulation model. The grain geometrical structure of reconstructed prior β grains along the build-up direction has a good agreement with the simulation result. The model can also be applied to other cases of MAM or combine with various models to achieve real-time as-solidified crystallographic feature prediction when it meets the scope of application. • Traditional solidification microstructure simulation methods all have their merits and drawbacks when applied in MAM cases. • Invasion model is established to investigate the transient competitive grain growth behavior within bi-crystal systems. • Wire DED Ti6Al4V thin-wall samples are used as a benchmark to test the validation of the novel simulation model.

Improvement of heat transfer performance and unmatched characteristics of a water-cooled carbon dioxide gas cooler

The problem of mismatch in temperature glides in a water-cooled carbon dioxide (CO2) gas cooler has attracted much attention. This paper quantifies the degree of mismatch in temperature glides using an unmatched coefficient and then investigates and improves the unmatched characteristics and heat transfer performance of direct heating (DH) gas coolers with a validated simulation model. The results show that for a DH gas cooler, two zones can be specified with the mass flow ratio of water to CO2. Zone I, with a low flow ratio, produces high-temperature hot water and shows good temperature-glide matching, but the CO2 exit temperature and heat transfer rate are unsatisfactory. Zone II, with a high flow ratio, achieves acceptable CO2 exit temperature and heat transfer rate, but its hot water temperature is low, and its matching characteristic is poor. A segmentation heating (SH) gas cooler is thus developed to compensate for the weaknesses of the two zones while preserving their advantages by selectively adjusting the water flow of a certain part of the gas cooler. The results indicate that an SH gas cooler can increase the heat transfer rate by 4.4% at most and reduce the CO2 exit temperature by 2.3 °C at most. The hot water temperature can be increased by 6.9 °C at most, and the unmatched coefficient can be decreased by 8.2% at most.

Snowfall Model Validation Using Surface Observations and an Optimal Estimation Snowfall Retrieval

AbstractIn the winter, orographic precipitation falls as snow in the mid- to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics. Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces +10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0%, +43.8%, and +59.2%, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by −1071.4% and −523.7%, respectively. The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in situ and vertically profiling remote sensing instruments to identify biases in numerical weather prediction.

Using simulation modeling to minimize patient-to-patient contact time while optimizing clinical operations during a pandemic

To the Editor: The COVID-19 pandemic significantly stressed outpatient operations. In March 2020, ambulatory visits declined by nearly 60%, and many practices closed to decrease transmission.1 As clinics reopened, they had to quickly readjust clinical operations—balancing telemedicine and in-person visits to minimize patient-to-patient contact (PTPC) during in-person visits.2,3 By leveraging a previously validated simulation model, we studied how to vary resources to minimize patient exposure risk while maximizing physician utility by maintaining patient volume and access during the COVID-19 pandemic.

A review of simulation training and new 3D computer-generated synthetic organs for robotic surgery education

We conducted a comprehensive review of surgical simulation models used in robotic surgery education. We present an assessment of the validity and cost-effectiveness of virtual and augmented reality simulation, animal, cadaver and synthetic organ models. Face, content, construct, concurrent and predictive validity criteria were applied to each simulation model. There are six major commercial simulation machines available for robot-assisted surgery. The validity of virtual reality (VR) simulation curricula for psychomotor assessment and skill acquisition for the early phase of robotic surgery training has been demonstrated. The widespread adoption of VR simulation has been limited by the high cost of these machines. Live animal and cadavers have been the accepted standard for robotic surgical simulation since it began in the early 2000s. Our review found that there is a lack of evidence in the literature to support the use of animal and cadaver for robotic surgery training. The effectiveness of these models as a training tool is limited by logistical, ethical, financial and infection control issues. The latest evolution in synthetic organ model training for robotic surgery has been driven by new 3D-printing technology. Validated and cost-effective high-fidelity procedural models exist for robotic surgery training in urology. The development of synthetic models for the other specialties is not as mature. Expansion into multiple surgical disciplines and the widespread adoption of synthetic organ models for robotic simulation training will require the ability to engineer scalability for mass production. This would enable a transition in robotic surgical education where digital and synthetic organ models could be used in place of live animals and cadaver training to achieve robotic surgery competency.

Development and validation of a novel 3D-printed simulation model for open oesophageal atresia and tracheo-oesophageal fistula repair

BackgroundThe role of simulation training in paediatric surgery is expanding as more simulation devices are designed and validated. We aimed to conduct a training needs assessment of UK paediatric surgical trainees to prioritise procedures for simulation, and to validate a novel 3D-printed simulation model for oesophageal atresia and tracheo-oesophageal fistula (OA-TOF) repair.MethodsA questionnaire was sent to UK trainee paediatric surgeons surveying the availability and utility of simulation. The operation ranked as most useful to simulate was OA-TOF repair. 3D-printing techniques were used to build an OA-TOF model. Content, face and construct validity was assessed by 40 paediatric surgeons of varying experience.ResultsThirty-four paediatric surgeons completed the survey; 79% had access to surgical simulation at least monthly, and 47% had access to paediatric-specific resources. Perceived utility of simulation was 4.1/5. Validation of open OA-TOF repair was conducted by 40 surgeons. Participants rated the model as useful 4.9/5. Anatomical realism was scored 4.2/5 and surgical realism 3.9/5. The model was able to discriminate between experienced and inexperienced surgeons.ConclusionUK paediatric surgeons voted OA-TOF repair as the most useful procedure to simulate. In response we have developed and validated an affordable 3D-printed simulation model for open OA-TOF repair.

An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging.

Over the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system's optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays. In this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray's specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse. We have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies. According to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.

Applied Calibration and Validation Method of Dynamic Process Simulation for Crushing Plants

There is a need within the production industry for digitalization and the development of meaningful functionality for production operation. One such industry is aggregate production, characterized by continuous production operation, where the digital transformation can bring operational adaptability to customer demand. Dynamic process simulations have the ability to capture the change in production performance of aggregate production over time. However, there is a need to develop cost-efficient methodologies to integrate calibrations and validation of models. This paper presents a method of integrating an experimental and data-driven approach for calibration and validation for crushing plant equipment and a process model. The method uses an error minimization optimization formulation to calibrate the equipment models, followed by the validation of the process model. The paper discusses various details such as experimental calibration procedure, applied error functions, optimization problem formulation, and the future development needed to completely realize the procedure for industrial use. The validated simulation model can be used for performing process planning and process optimization activities for the crushing plant’s operation.

Relating simulation studies by provenance—Developing a family of Wnt signaling models

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?

Identifying an efficient and fair allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. The recent heart allocation rule by the United Network for Organ Sharing (UNOS) has emphasized medical urgency to address the heart transplant crisis by further dividing the previous sickest patient group into three subgroups. However, there is a significant debate on optimality and fairness of this policy because although it can help reduce pre‐transplant mortality, it may reduce post‐transplant survival. We undertake a rigorous study to address this debate by measuring the impacts of a variety of perspectives on the waiting list and patients. We show that the optimal policy of our proposed fluid model is a dynamic priority rule, and provide insight on the impact of fairness constraints on such priorities. We quantify the price that the society pays for following a medical urgency approach, which favors the sickest patients, compared to a utilitarian approach, which seeks to maximize total quality‐adjusted life years (QALYs). Our results, produced by a validated simulation model, reveal that the said price is 7.7% of total QALYs and increases to 11% by considering a broader regional sharing aligned with four‐hour heart cold ischemic time. We study other relevant objectives/measures in transplantation and our results show that the utilitarian policy outperforms the medical urgency policy in other measures as well. Our analysis provides novel insights on optimal patient allocation and sheds light on the debate around this challenging problem.

Computationally efficient source grid selection and source interpolation in computational aeroacoustics applied to an axial fan.

The noise generation of an axial fan is mainly caused by flow-induced noise and can therefore be extracted from its aeroacoustics. To do so, a hybrid approach separating flow and acoustics is well suited due to its low Mach number. Such a computationally efficient hybrid workflow requires a robust conservative mesh-to-mesh transformation of the acoustic sources as well as a suitable mesh refinement to guarantee good convergence behavior. This contribution focuses on the mesh-to-mesh transformation, comparing two interpolation algorithms of different complexity towards the applicability to the aeroacoustic computation of an axial fan. The basic cell-centroid approach is generally suited for fine computational acoustic (CA) meshes and low phase shift, while the more complex cut-volume method generally yields better results for coarse acoustic meshes. While the cell-centroid interpolation scheme produces source artifacts inside the propagation domain, a grid study using the grid convergence index shows monotonic convergence behavior for both interpolation methods. By selection of a proper size for the source grid and source interpolation algorithm, the computational effort of the experimentally validated simulation model could be reduced by a factor 4.06.

To be or not to be a target: that is the question to manage mixed fisheries

Abstract Simulation models are used extensively to evaluate the performance of fisheries management strategies, though they must be validated to ensure that they accurately represent the real system. One of the quantitative methods available to advance in the process of validating simulation models is global sensitivity analysis (GSA). However, its use in fisheries management has been very limited. When GSA is applied to management strategy evaluation implementations, it can also help manage available resources efficiently with respect to uncertainty in the management process and the conditioning of simulation models. Mixed-fisheries management plans were recently implemented for demersal fisheries in the Northeast Atlantic, which were evaluated previously using complex bioeconomic models. Here, we applied GSA to the model used in Iberian waters, employing an efficient model design to introduce uncertainty in every single input factor. While most biological factors contributed significantly to the variance of results, only few economic factors did. Moreover, we found that increasing accuracy in the stock assessment process would mainly impact management advice and that only the management of target stocks had a real impact on the system. This highlights the importance of properly managing hake (Merluccius merluccius), which is currently managed using an empirical harvest control rule.

Assessment of control tools for utilizing excess distributed photovoltaic generation in domestic electric water heating systems

Appliance level control and automation is an increasingly promising demand-side management tool with growing installation of advanced metering, monitoring and control infrastructure in both residential and commercial contexts. Successful implementation of appliance control and automation can alleviate network peak demand and improve distributed photovoltaic (D-PV) self-consumption to reduce its network voltage and reverse power flow impacts. Domestic electric water heating (DEWH) systems are widely deployed globally and have one of the highest peak power draw and overall energy consumption of household appliances. DEWH storage tanks offer large thermal energy storage capacity which can be used for shifting demand to lower demand periods. With growing D-PV deployment, they also offer the opportunity to store excess generation that would be otherwise exported to the grid. In this work, an intelligent water heating control tool (IWHC) is developed to store excess D-PV generation in DEWH storage tanks as thermal energy, according to the D-PV generation characteristics, household electricity consumption, hot water draw (HWD) patterns, and real time energy monitoring. The IWHC tool was installed and tested in nine Australian households with D-PV and DEWH systems. For performance comparison, two other commercially available control tools, timer, and diverter, were installed and tested in eleven other households with D-PV and DEWH systems. For each control tool, energy simulation models were developed, and the collected field performance data was used to validate the models. The validated simulation models were extended to a broader set of 380 Australian households with a year of D-PV, household and DEWH electricity consumption data. The results indicate that, on average, households can utilize 2.4 kWh, 1.8 kWh and 3.4 kWh of daily excess D-PV generation for water heating, using the IWHC, timer and diverter, respectively. Financial savings from the control of DEWH are highly dependent on households’ tariffs and daily HWD profiles. Under the most optimal morning dominant HWD profile scenario and with an average tariff, households can, on average, save $100, $80, and $170 per year with the IWHC, timer and diverter, respectively. However, the diverter’s superior field performance comes with higher capital cost, making IWHC the most attractive option.

Flow mechanism of bimodal phenomenon in intermittent corner separation in a linear compressor cascade using a hybrid RANS/LES method

To gain a better knowledge of the bimodal phenomenon in intermittent corner separation, a hybrid RANS/LES turbulence model is applied to simulate the unsteady flow in a linear cascade. After the validation of the Stress Blended Eddy Simulation (SBES) model with the detailed experimental data, the flow mechanisms of intermittent coherent vortical structures and bimodal phenomenon are clarified according to unsteady numerical results. An indicator is proposed to evaluate the degree of the bimodal phenomenon. Firstly, the intermittency in corner separation is found, which arises from the formation of coherent vortical structures. The collision of the boundary layer and backflow near the trailing edge gives rise to large-scale coherent vortices around the separation surface. Then, according to the histogram of velocity probability density functions (PDF), the bimodal phenomenon found near the separation surface indicates that there are two non-periodic switching flow modes. Combination of vortical structures and velocity distributions manifests that the intermittency of coherent vortical structure is responsible for the switching flow modes around the separation surface. Thirdly, three characteristic parameters are defined to describe the features of two flow modes, corresponding to two Gaussian distributions that PDF decomposed into. The indicator Ra, which measures the difference between two flow modes, is used to evaluate the degree of the bimodal phenomenon. A larger Ra represents the higher probability of bimodal phenomenon occurring in flow fields, which can be used to quantitatively determine the position and fluctuation of the corner separation surface.

Economic Optimization of Rotary Heat Exchangers Using CO2 Pricing Scenarios Based on Validated Fluid Dynamic and Thermodynamic-Based Simulation

Rotary heat exchangers have been widely used in paint shops, combustion power plants, and in heating, ventilation, and air conditioning systems in buildings. For these processes, many types of heat exchangers are available in the market: Tube-shell heat exchangers, plate heat exchangers, and rotary heat exchangers, among others. For the rotary heat exchangers, the problem is that there is no net present value method and lifecycle assessment method-based optimization found in the literature. In this work, we address this issue: An optimization is carried out with help of an empirically validated simulation model, a life-cycle assessment model, an economical assessment, and an optimization algorithm. The objective function of the optimization simultaneously considers economic and environmental aspects by using different CO2 pricing. Different CO2 pricing scenarios lead to different optimization results. The ambient air empty tube velocity va, 2.1 optimum was found at 1.2 m/s, which corresponds to a specific mass flow msp of 5.4 kg/(m2·h). For the wave angle β, the optimum was found in the range between 58° and 60°. For the wave height h* the optimum values were found to be between 2.64 mm and 2.77 mm. Finally, for the rotary heat exchanger length l, the optimum was found to be between 220 mm and 236 mm. The optimization results show that there is still potential for technical improvements in the design and operation of rotary heat exchangers. In general terms, we recommend that the optimized rotary heat exchanger should cause less pressure drop while resulting in similar heat recovery efficiency. This is because the life cycle assessment shows that the use phase for rotary heat exchangers has the biggest impact on greenhouse gases, specifically by saving on Scope 2 emissions.