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Gaseous and semi-volatile organic compounds emitted by the transport sector contribute to air pollution and have adverse effects on human health. To reduce harmful effects to the environment as well as to humans, renewable and sustainable bio-hybrid fuels are explored and investigated in the cluster of excellence "The Fuel Science Center" at RWTH Aachen University. However, data on the effects of bio-hybrid fuels on human health is scarce, leaving a data gap regarding their hazard potential. To help close this data gap, this study investigates potential toxic effects of a Ketone-Ester-Alcohol-Alkane (KEAA) fuel blend on A549 human lung cells. Experiments were performed using a commercially available air-liquid interface exposure system which was optimized beforehand. Then, cells were exposed at the air-liquid interface to 50–2000 ppm C3.7 of gaseous KEAA for 1 h. After a 24 h recovery period in the incubator, cells treated with 500 ppm C3.7 KEAA showed significant lower metabolic activity and cells treated with 50, 250, 500 and 1000 ppm C3.7 KEAA showed significant higher cytotoxicity compared to controls. Our data support the international occupational exposure limits of the single KEAA constituents. This finding applies only to the exposure scenario tested in this study and is difficult to extrapolate to the complex in vivo situation. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is critical, and Carbon Capture, Storage, and Utilization (CCSU) technologies can play a vital role. However, the energy needed for compression is a major obstacle to the success of these technologies. Thus, designing compressors with optimal aerodynamic and structural efficiency is essential. Therefore, the present work proposes an application of a structural optimization process for centrifugal compressors to achieve a more efficient and failure-resistant machine. Our optimization process primarily focuses on minimizing resonance risks, ensuring the static structural integrity. We employ pre-stressed modal analysis, accounting for factors like inertial loads, centrifugal stiffening, spin-softening, and gyroscopic/Coriolis effects. Objective functions for optimization are based on Campbell Diagram. Gaussian Process Regression is employed, and the training process is conducted iteratively using an implemented adaptive Bayesian Sampling method. Three optimization algorithms are utilized: the Genetic Algorithm, Particle Swarm Optimizer, and the Grey Wolf Optimizer. To validate our approach, we conduct three case studies, including two CO 2 centrifugal compressors intended for CCSU projects. In the axial blisk case, we successfully eliminated all resonance conditions, achieved a 23% reduction in mass, and maintained stress levels similar to the baseline. For the first-stage centrifugal compressor, we increased the bursting margin by 4.31% and eliminated critical resonance conditions. In the fourth-stage case, we improved low-frequency resonance conditions, although without a significant enhancement in structural integrity compared to the baseline design. Despite the reduction in resonance risk, frequency tuning for the centrifugal compressors proved to be a challenging task, and further improvements are still necessary. [ABSTRACT FROM AUTHOR]

Floating production storage and offloading (FPSO) platforms are great energy consumers. Reservoir fluid composition and operating parameters of FPSOs are known to be important parameters for the total energy demand, and also for enhancing overall oil production. In the current research paper, the contribution of eight thermodynamic input parameters of a typical Brazilian FPSO operating on a pre-salt field is quantified by the Smoothing Spline ANOVA method for two responses: (i) energy consumption of the plant and; (ii) overall oil production. Three operation modes are considered in the analysis: Mode 1 (maximum oil/gas content), Mode 2 (50% BS&W) and Mode 3 (maximum water/CO2 content). The input parameters considered in the present research are: output pressure of the first control valve (P1), output pressure of the second stage of separation train (P2), input pressure of the third stage of separation train (P3), output pressure of dilution water pump (P4), output pressure of the main gas compressor (Pc), petroleum temperature of the first heat exchanger (T1), petroleum temperature of the second heat exchanger (T2), and dilution water temperature of heat exchanger (T3) are considered as input parameters. In terms of energy consumption, parameters P3 and Pc are always relevant for all operation modes. Furthermore, P3, P2, and T2 are the most important parameters for overall oil production for all operation modes. [ABSTRACT FROM AUTHOR]

A Floating, Production Storage and Offloading (FPSO) plant is a high-energy consumer (from a few to several hundreds of megawatts). Since a number of parameters have effects on the FPSO plant performance, screening analysis procedure could be used to select the most important parameters affecting a given output and an optimization procedure being applied to maximize/minimize an objective function. Thus, optimization procedures focused on fuel consumption and hydrocarbon liquids recovery can improve the energy efficiency, product recovery, and sustainability of the plant. In the present work, optimization procedures are used for an FPSO plant operating at three different conditions of the Brazilian deep-water oil field in pre-salt areas to investigate: (1) Maximum oil/gas content (Mode 1); (2) 50% BS&W oil content (Mode 2) and; (3) High water/CO 2 content in oil (Mode 3). In order to reduce the computational efforts, we investigate the contribution of eight thermodynamic input parameters to the fuel consumption of the FPSO plant and hydrocarbon liquids recovery by using the Smoothing Spline ANOVA (SS-ANOVA) method. From SS-ANOVA, the input parameters that presented the major contributions (main and interaction effects) to the fuel consumption and hydrocarbon liquids recovery were selected for the optimization procedure. The optimization procedure consists of a Hybrid method, which is a combination of Non-dominated Sorting Genetic Algorithm (NSGA-II) and AfilterSQP methods. The results from the optimized case indicate that the minimization of fuel consumption is 4.46% for Mode 1, 8.34% for Mode 2 and 2.43% for Mode 3, when compared to the baseline case. Furthermore, the optimum operating conditions found by the optimization procedure of hydrocarbon liquids recovery presented an increase of 4.36% for Mode 1, 3.79% for Mode 2 and 1.75% for Mode 3 in total exportation oil. • For the first time, a lifetime FPSO optimization is performed considering three operation modes. • The fuel consumption of the FPSO plant is reduced by 8.34%. • The hydrocarbon liquids recovery is increased by up to 4.36%. • Real data of an FPSO operating in the Brazilian pre-salt basin has been used. [ABSTRACT FROM AUTHOR]

Abstract The present work aims to perform a parametric study of heat transfer enhancement and the associated pressure loss applied to louvered fins with rectangular winglet vortex generators (RWL). The contributions of a set of design variables on heat transfer and pressure drop are evaluated for two geometry types, L1 and L2. The procedure to investigate the relative importance of the geometrical design variables on thermal-hydraulic performance is enabled by a Design of Simulations (DOS) method called Latin Hypercube Sampling (LHS) in association with a modern non-parametric statistical method (Smoothing Spline ANOVA method) and Computational Fluid Dynamics. Outcomes from the screening analyses turned out that the louver angle is the unique contributor to friction factor for both geometry types and this behaviour is independent of Reynolds number. With respect to heat transfer, the contributions of the input variables are different for both geometry types and Reynolds numbers. For Re D h = 120 , no important two-factor interaction effects were observed for both L1 and L2, in the same way as occurred in terms of friction factor. Conversely, for Re D h = 240 one relevant interaction effect was observed. [ABSTRACT FROM AUTHOR]

The demand for LNG (liquefied natural gas) increases each year, and relevant studies have been developed in order to reduce both power consumption and greenhouse gas emissions of LNG plants. Multistream plate-fin heat exchangers (MPFHE) are widely used in these plants due to their large heat transfer surface area per unit volume for multi-phase flows. The thermal-hydraulic performance of heat exchangers also impacts on compressor shaft work of liquefaction plants. In this study, an automated optimization procedure is performed for commonly used MPFHE in a three-stage propane pre-cooling cycle to maximize the coefficient of performance (COP) of liquefaction plants. Four geometric input parameters (fin type, fin height, fin thickness, and fin frequency) have been selected for the optimization. The optimal heat exchangers and operating conditions of the entire refrigeration cycle and their feasibility are taken into account. The optimal fin designs of the heat exchangers affect the cycle operating conditions, which results in COP improvements of 18.9%, 2.5%, and 9.4%, and ranging from 7% to 32% of mitigation in CO 2 emission when compared to the literature cases. Compared with the baseline case, the COP is improved by 9.4%, compressor shaft power is reduced by 7.1% (about 11.2 MW), heat transfer is augmented by 1.5% (3.8 MW), and CO 2 emission is reduced by 6.3 t/h, which result in more efficient and sustainable LNG production. • The pre-cooling cycle operational conditions and the MPFHE fin parameters were simultaneously optimized. • The operational feasibility of the optimal heat exchangers is guaranteed during the optimization procedure. • The optimal solution leads to a reduction in CO 2 emission of up to 38.3 t/h. • The optimal solution results in COP improvement of up to 18.9%. [ABSTRACT FROM AUTHOR]

Abstract FPSO (Floating, Production, Storage and Offloading) units are used for primary petroleum processing. The FPSO units have a technical advantage for short-lived well exploration and for remote marginal fields, where building a pipeline is cost-prohibitive. Considering the relevance of the FPSOs for the Brazilian petroleum market, the main goal of this paper is to present an optimization procedure to find the best operating conditions of a primary petroleum platform processing crude oil with high gas-oil ratio (GOR) and moderate CO 2 content. Seven input parameters related to operating pressures of plant are selected for the optimization procedure aiming to minimize the total fuel consumption (FUE) of the FPSO. For the optimization procedure, the processing plant of the FPSO is modeled and simulated by using Aspen HYSYS®, and the Genetic Algorithm is used for minimizing the objective function. The optimum operating condition found by the optimization procedure presented a reduction in fuel consumption by 4.6% when compared to a conventional operating condition. Moreover, because of the fuel consumption optimization, a significant reduction by 6.3% in the power consumption of the plant and an improvement in the recovery of the volatile components were verified, in comparison with the baseline case. Highlights • A FPSO processing crude oil with high GOR and moderate CO2 content was modeled and optimized. • Real performance data of an offshore gas turbine was considered. • The integration of Aspen HYSYS with an external optimizer based on Genetic Algorithm was conducted. • Significant reductions of fuel consumption and CO 2 emission were achieved by optimization. • Real data of a FPSO operating in the Brazilian pre-salt basin was used. [ABSTRACT FROM AUTHOR]

Fluid flow in centrifugal compressors is complex and turbulent, making it difficult to achieve a robust equipment design. In this work, a computational fluid dynamics (CFD) analysis is conducted on the periodical domain of a CO2 centrifugal compressor impeller and vaneless diffuser, with a 2.85:1 pressure ratio, using ANSYS CFX. The fluid flow is assumed to be steady state, turbulent, and three dimensional. Since polar angles are not considered in the traditional one-dimensional analysis, eight polar angles on hub and shroud were considered for modeling, sensitivity analysis, and optimization. After numerical verification and validation, a sequential sensitivity analysis (SA) was performed to identify non-influential variables. From the same design of experiment (DoE) used by the Morris qualitative SA, a response surface (RS) was trained to perform a quantitative SA by the smoothing spline ANOVA (SS-ANOVA) method. The Morris method was found to be more conservative than SS-ANOVA, keeping more variables as influent for the analysis. Both methods agreed on influential variables ranking. Low computational effort was required to submit the RS to a constrained optimization procedure using the NSGA-II method. The polytropic efficiency of the optimal centrifugal compressor configuration increased 0.7%, keeping the pressure ratio above 2.85, and the required power and outlet temperature below the base compressor. The impact of the polar angles at trailing edge on output variables is higher than leading edge. The optimal centrifugal compressor found is submitted to different mass flow rates and the overall performance for the optimal angles of the trailing edge and leading edge of the impeller was higher than the base compressor. The strategy adopted herein related to qualitative and quantitative sensitivity analysis coupled with response surface and the constrained optimization was shown to be robust, which can be applied to high-dimensional CFD models to reduce the computational cost with suitable results regarding fluid flow phenomena. [ABSTRACT FROM AUTHOR]