Parameter Identification Process Research Articles

Intelligent Design of Robotic Welding Process Parameters Using Learning-Based Methods

With the wide application of multi-layer and multi-pass welding in industry, the traditional manual welding method is difficult to meet the needs of manufacture. Welding Robot has the advantages of stable productivity, ensuring welding quality even in special environment, so the welding robots are used at a growing trend in manufacturing fields to complete different welding tasks. In this paper, an intelligence learning method for welding robot is designed, aiming at the prediction of welding process parameters and bead geometry parameters in the welding process, deep and machine learning algorithms are used for realization. It provides an instruction for the design of process parameters to realize the intellectualization and automation of welding robot. The experimental results show that automatic parameters learning based on machine learning are effective and different learning methods should be selected for different process parameter prediction tasks in order to achieve the best prediction effect.

Modeling Rogowski Coils for Monitoring Surge Arrester Discharge Currents

Rogowski coils (RCs) are widely used to measure power or high frequency currents based on their design. In this paper, two types of RCs that are circular (traditional) and cylindrical shapes wound using wire covered by varnish are constructed. This construction is carried out to be suitable for monitoring the discharge current of the surge arrester installed in the distribution system. Concerning high frequency RC modeling for both types considering transfer function is introduced. Self-integrating for both types is attained. Therefore, the experimental tests using function generator for both coils are carried out to identify the parameters of the transfer function representing the introduced model. The measured signals for current and induced voltages are denoised for the parameter identification process. The denoised process is achieved using the MATLAB code ‘wdenoise’ while the parameters are estimated using the system identification toolbox. Verification of the proposed model is achieved using experimental results for the two coils. The sensitivity of the two coils is investigated based on the induced output voltage. The application concerning the two coils for monitoring the discharge current of the surge arrester is done. The results confirm the accuracy of the introduced RC model, as well as the performance of the cylindrical shape, is better than the traditional one. The simulation is carried out using MATLAB and ATPDraw programs.

Resonating Power Decoupling Using Multifunctional Bidirectional DC/DC Converter in Hybrid Railway Traction Application

For the single-phase high-power railway traction system, the inevitable second-order resonating power has become a critical issue to the traction system. In this article, a power-decoupling solution is proposed to replace the conventional passive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> resonance filter by utilizing the buck-type bidirectional dc/dc converter (BBDC) of hybrid electric multiple units, which is designed to power the train in nonelectrified routes using the on-board battery. By considering both the current ripples in the battery mode and the power decoupling in the catenary mode, the corresponding parameter design process of the BBDC is presented. Based on the power coupling phenomenon and the spectrum characteristic of the BBDC, a direct resonance control method is proposed to decouple the low-order resonating power. Simulations and experiments are carried out to validate the effectiveness of the new proposal. The results demonstrate that the proposal is almost the same as using the conventional passive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> resonance filter for power decoupling in both steady and dynamic operation scenarios.

Detuned Resonant Capacitors Selection for Improved Misalignment Tolerance of LCC-S Compensated Wireless Power Transfer System

This study proposes a detuned resonant capacitor selection strategy to eliminate the influence of the changed coupling coefficient on the transferred power and efficiency of a wireless power transfer (WPT) system using the LCC-S resonant topology. The output characteristics of the conventional tuned LCC-S system and the novel detuned system are analyzed and compared in detail, the different effects of the detuned parameters on the system output performance are expounded by simulations, and an optimal detuned parameter design process is presented. By adjusting the compensated capacitors on the transmitting side, the output voltage can be constant under varying coupling conditions without changing the switching frequency. Experiments with a 1.5kW power scale were conducted to verify the proposed theoretical analysis. The results show that the fluctuation of the output voltage is restricted to 8.2% in the optimized system when the coupling coefficient changes from 0.18 to 0.38. Under the rated coupling conditions, the experimental results demonstrate that compared with ZPA (88.29%) and ZVS (90.66%), ZCS (91.52%) operating conditions are more suitable for a WPT system equipped with IGBTs to improve the overall efficiency.

An Augmented Reality Framework for Visualization of Internet of Things Data for Process Supervision in Factory Shop-Floor

Modern industrial environments provide a high level of digitalization, enabling great volumes of information to be used for shopfloor monitoring and control purposes. In cases where human operators participate in the production process, it is important to support operator’s intervention with Internet of Things (IoT) data and empower process supervision with constant information over Key Performance Indicators (KPIs). Towards this end, the proposed framework exploits Augmented Reality (AR) for the provision of an IoT data visualization solution to support KPIs based process supervision in the smart factory environment. The solution utilizes IoT-enabled components, in order to receive dynamic information over the status of sensors and resources spread across the factory shop-floor. The AR application facilitates the need for visualization of information available on the network, on the actual shopfloor components, which offer transparency and ease the supervision of production processes. The development of the AR and cloud API has been based on CSharp and Java, in a reusable framework that can be reused for similar industrial cases. The reconfigurability and reuse of the AR solution is supported by a template configuration, allowing industry practitioners to configure application features and external connections without any additional software development. The solution was validated on a thermoforming process supervision case of a white goods production system. The main goal was to use the AR framework to support operators during the process parameters design phase, and dynamic supervision of the shopfloor environment. It is demonstrated that the proposed system can be used for KPIs monitoring of the production cycles and for empowering process supervision and optimization.

Hot-deformation characteristics of Al-alloyed δ-ferritic and martensitic dual-phase steel

The hot working behavior of Al-alloyed δ-ferritic/martensitic dual-phase steel was investigated using a Gleeble-3800 thermomechanical simulator and compression tests at deformation temperatures and strain rates ranging from 800 to 1100 °C and 0.1 to 3 s−1, respectively. The shape of the flow stress curve of the experimental steel resembled that of traditional dynamic recovery (DRV) flow stress curves produced by work hardening, ferritic DRV, ferritic dynamic recrystallization (DRX), and austenitic dynamic phase transformation (DPT). Additionally, constitutive equations were established to predict the steady-state stress. The apparent deformation activation energy of the steel was approximately 264.7 kJ/mol. Moreover, the microstructure following deformation was investigated using electron backscatter diffraction. The primary dynamic restoration mechanisms of δ-ferrite comprised DRV and DRX at temperatures below and above 900 °C, respectively. However, with increasing deformation temperature and strain rate, the primary dynamic restoration mechanism of δ-ferrite shifted from continuous DRX (CDRX) to discontinuous DRX (DDRX). Additionally, the processing maps of the steel were established using the dynamic materials model. Further, based on the microstructure results, it was found that the power dissipation efficiency of ferritic CDRX, ferritic DDRX, ferritic DRV, and austenitic DPT gradually decreased. Additionally, only one domain of flow instability was identified, and it was located at 1025–1075 °C/0.6–3 s−1. Thus, to avoid unstable deformation regions, a two-stage design of the optimal process parameters for industrial processing is highly essential.

An Offline Parameter Self-Learning Method Considering Inverter Nonlinearity With Zero-Axis Voltage

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis.

Identification of submarine landslides in the Colombian Caribbean Margin (Southern Sinú Fold Belt) using seismic investigations

Submarine landslides can be triggered by several processes and involve a variety of mechanisms. These phenomena are important sediment transport processes, but they also constitute a significant geohazard. Mapping of the southwestern Caribbean Sea using 3D seismic data has allowed identification of several submarine landslides in the Colombian Margin in the area dominated by the Southern Sinú Fold Belt (SSFB). A poststack depth-migrated seismic cube survey with a 12.5 by 12.5 m bin spacing was used to identify landslides in an area covering 5746 km2. Landslides were interpreted using a seafloor morphologic parameter identification process and the internal deformation of the slope-forming material, as seen from seismic data. A total of 93 landslides were identified and classified based on their movement styles as follows: 52 rotational, 29 translational, and 12 complex landslides. In addition, 12 distinct deformational zones and a zone of mass transport complex (MTC) were identified. Five different ground condition terrains were interpreted based on landslide type and distribution as well as in geologic structures and seismic reflection analysis. Two main processes seem to influence landslides in the study area. First is the folding and faulting involved in the SSFB evolution. This process results in oversteepened slopes that start as deformational zones and then fail as translational or rotational slides. Those individual landslides progressively become complex landslide zones that follow geologic structural orientation. Second is the continental shelf break erosion by debris flows, which fills in intraslope subbasins and continental rise with several MTCs. According to the results, risk of damage by landslides increases in distances shorter than 4 km along structural ridge foothills in the study zone.

Process parameters design method of drainage gas recovery technology in gas-driven pump for coalbed methane production

The main types of non-rod pumps such as the electrical submarine, piston, and jet pumps often have high discharge capacities. In this paper, the process parameters design method of a new non-rod gas-driven pump is proposed for carrying coal power, low discharge capacity, and low lift height drainage gas recovery in coalbed methane production. The gas-driven pump is driven by high-pressure gas and is mainly composed of a liquid storage chamber, liquid inlet valve, and liquid outlet valve. The change in gas pressure simultaneously opens or closes the liquid inlet and outlet valves, leads the liquid into the pump, and then discharges it. When the pressure of driven gas is below the lower pressure limit, the liquid inlet valve opens to allow the liquid to flow into the pump. When the pressure of driven-gas is above the upper-pressure limit, the liquid outlet valve opens to allow the liquid to drain from the pump. Based on the working mechanism of the gas-driven pump, we established a calculation model for the lower and upper limits of the driven gas pressure where the liquid inlet and outlet valves open respectively. Furthermore, we determined a model to calculate the pressure changes in the high-pressure gas tank on the ground during the drainage process. The engineering application on the pilot test wells shows that the gas-driven pump production system works well in liquid production and is a novel type of drainage gas recovery in coalbed methane production. The new calculation model was established as the solution to this lift processing scheme.

Profile Design of the Grooved Die and Rolling Force Prediction in the Cold Pilger Rolling Process

The objective of this study was to design the die groove profile and predict the rolling force produced when employing the variable curvature rolls and mandrel for manufacturing seamless pipes using the cold pilger rolling process. The parameters of the key process design were the diameter of the initial tube and final product, as well as the feed amount, reduction area, principal deformation zone, and roller radius. The rolling forces during the pilger rolling process were theoretically calculated to enable their prediction, and the characteristics of the cold pilger rolling process were identified. The calculated values were in close agreement with the experimental data. The die groove design is important in the prediction process because the dimensional accuracy of the tubes and the life of the dies are highly dependent on this design. The presented design method can be successfully applied to fulfill this objective. The tube shape and adequate tolerance can be attained by using the proposed design method. The mechanical properties of the pipe are evaluated by calculating the Q factor.

Performance of functionalized ionic liquid with double chemical sites for separating phenolic compounds: mechanism and liquid-liquid behavior studies

Ionic liquids (ILs) are recognized as green solvents in separation field. Many studies have found that the hydrogen bond between anions and solutes plays an important role during the separation process, while the cations in ILs do not. In this work, a functionalized [OHEmim][NO3], 1-hydroxyethyl-3-methylimidazole nitrate, with double chemical sites, is designed for the separation of phenolic compounds (phenol, o-, m-, p-cresol). The hydroxyl group is introduced into the cation as a chemical site. Meanwhile, the anion is also a chemical site. Thus, both the anion and the cation can interact with phenolic compounds. To verify this view, the separation performance of the IL was evaluated by mechanism analysis and liquid-liquid equilibrium behavior. Then, the σ-profiles, bond length, and interaction energy between [OHEmim][NO3] and phenolic compounds are calculated and compared with [Emim][NO3]. The liquid-liquid equilibrium (LLE) data of n-hexane + (phenol, o-, m-, p-cresol) + [OHEmim][NO3] ternary systems were determined. The separation coefficient (S) and distribution coefficient (D) were calculated to evaluate the extraction performance. Non-random two liquid (NRTL) model was adopted to correlate the experimental data, which can provide basic data and parameters for the extraction process design.

The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a 0.10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.

A Nonlinear Model and Parameter Identification Method for Rubber Isolators under Shock Excitation in Underwater Vehicles

Rubber isolators are usually used to protect high-precision equipment of autonomous underwater vehicles (AUVs), avoiding damage from overlarge dynamic excitation. Considering the nonlinear properties of the rubber material, the nonlinear behavior of rubber isolators under shock exaltation is hard to be predict accurately without the available modal and accurate parameters. In view of this, the present study proposes a nonlinear model and parameter identification method of rubber isolators to present their transient responses under shock excitation. First, a nonlinear model of rubber isolators is introduced for simulating their amplitude and frequency-dependent deformation under shock excitation. A corresponding dynamic equation of the isolation system is proposed and analytically solved by the Newmark method and the Newton-arithmetic mean method. Secondly, a multilayer feed-forward neural network (MFFNN) is constructed with the current model to search the parameters, in which the differences between the estimated and tested responses are minimized. The sine-sweep and drop test are planned with MFFNN to build the parameter identification process of rubber isolators. Then, a T-shaped isolator composed of high-damping silicon rubber is selected as a sample, and its parameters were determined by the current identification process. The transient responses of the isolation system are reconstructed by the current mode with the identified parameter, which show good agreement with measured responses. The accuracy of the proposed model and parameter identification method is proved. Finally, the errors between the reconstructed responses and tested responses are analyzed, and the main mode of energy attenuation in the rubber isolator is discussed in order to provide an inside view of the current model.

A parametric design process model for box culverts

This research proposes a parametric design process model to improve the structural engineering project team performance by automating the design and three-dimensional modelling procedures of box culverts. Although standardised design procedures can reduce the design time of repetitive structures such as box culverts, the increased time and effort required for revising construction drawings negatively impacts a project's performance. A literature review was conducted to develop a theoretical process model to improve the current structural design optimisation and three-dimensional modelling procedures of box culverts. The proposed process model was validated using structured interviews with professionally registered structural engineers for appropriateness to box culverts and the potential to improve project performance. The data analysis revealed that the interviewed engineers were in favour of automating the design optimisation and three-dimensional modelling procedures of box culverts. Moreover, parametric design automation would result in improved project performance when encountering an inevitable design change. However, the user's control over the output of each process should not be discarded. This study can help readers understand the transformation of the structural design and three-dimensional modelling procedures of repetitive structures, such as box culverts, into an algorithmic form to achieve improved project performance.

Removal of phosphate from aqueous solution by chitosan coated and lanthanum loaded biochar derived from urban dewatered sewage sludge: adsorption mechanism and application to lab-scale columns.

A lanthanum modified sludge biochar chitosan (La-SBC-CS) microsphere was successfully synthesized by dropping sludge biochar (BC) and chitosan into a lanthanum chloride solution. Batch adsorption experiments were conducted to investigate the adsorption kinetics and isotherm. Application of continuous phosphate removal was achieved via lab-scale column reactors. The phosphate adsorption equilibrium data of the La-SBC-CS fitted well with the Freundlich isotherm, with a maximum adsorption amount of 81.54 mg p/g at 25 °C. Characterization of the adsorbent using scanning electron microscopy analysis (SEM), X-ray energy spectrum analysis (EDS), X-ray diffraction analysis (XRD) and Fourier infrared analysis (FTIR) techniques suggested that the possible adsorption mechanisms were electrostatic interaction, ligand exchange and complexation. The La-SBC-CS kept 76.37% phosphate removal efficiency after eight recycles. The results of continuous column reactor experiment demonstrated that the breakthrough time increased with an increase in adsorbent filling height, while it decreased with an increase in initial phosphate concentration or flow velocity. The Yoon model was applied to the continuous experimental data to predict breakthrough curves and determined the characteristic adsorption parameters for process design. This study indicated the potential for the practical application of La-SBC-CS in phosphate removal from wastewater.

Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

BackgroundThe prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness.ResultsAn open dataset of macular degeneration images (https://data.mendeley.com/datasets/rscbjbr9sj/3) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance.ConclusionThe high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.

Experimental Study on the Drilling Parameter Analysis of Banana Fiber Reinforced Vajram Mixed Phenolic Resin Composite Laminates

ABSTRACT Drilling operation of fiber reinforced polymeric (FRP) composites is significantly dissimilar from conventional drilling operation of metals; hence, identification of optimum drilling process parameters for FRP composites has become mandatory. In this work, drilling parameter optimization for banana fiber reinforced Vajram mixed phenolic resin composite has been carried out using Taguchi method. Three drilling input parameters such as point angle, spindle speed, and feed rate were optimized for two output responses, namely, thrust force and torque. Two composite samples were fabricated with 5% NaOH-treated banana fiber at 30% fiber volume ratio; sample 1 with 100% phenolic resin and sample 2 with 80% Vajram + 20% phenolic resin. Signal-to-noise ratios for all the output responses were calculated, and other statistical analysis were carried out. Highly contributing factor for sample 1 thrust force was point angle with 64.34% contribution and for torque was feed rate with 28.43% contribution. Highly contributing factor for sample 2 thrust force and torque was point angle with 60.47% and 41.37%, respectively. The residuals are fall on near straight line which ensures the normal distribution of errors; hence, no abnormality was noted on any analysis.

Real-time heat dissipation model of electronic equipment for determining the dynamic cooling demand of office buildings

Internal heat gains are necessary inputs for determining the required cooling demand of Heating, Ventilation, and Air-Conditioning (HVAC) systems in office buildings. Electronic equipment is one of the main indoor thermal sources which needs to be carefully investigated because of its large heat dissipation and significant impact on the HVAC system. So far, very few studies about calculating the real-time heat dissipation of the equipment have been conducted. To address this problem, a real-time heat dissipation model of electronic equipment in office buildings is proposed in this paper. First, the structure of real-time heat dissipation model of electronic equipment was studied according to energy conservation laws, Laplace transform method, etc., and the model structure was determined as an eleventh-order transfer function. Second, an experimental setup was established to collect data on the heat dissipation and electric power consumption of typical electronic equipment, which were used for the parameter identification and model verification processes. Third, the unknown parameters in the real-time heat dissipation model were identified, and after order reduction analysis, a third-order transfer function was chosen as the heat dissipation model. Finally, experimental data and the designer's simulation toolkits were used to evaluate the proposed model's accuracy, yielding a maximum mean absolute percentage error of 9.13%. The presented model not only describes the dynamic and stochastic characteristics of the heat dissipation of electronic equipment, but also exhibits great importance in calculating instantaneous cooling demand and saving energy.

New Experimental Approach for the Determination of the Heat Generation in a Li-Ion Battery Cell

With regard to safety, efficiency and lifetime of battery systems, the thermal behavior of battery cells is of great interest. The use of models describing the thermoelectric behavior of battery cells improves the understanding of heat generation mechanisms and enables the development of optimized thermal management systems. In this work, a novel experimental approach is presented to determine both the irreversible heat due to ohmic losses and the reversible heat due to entropy changes directly via heat flow measurements. No additional information about thermal properties of the battery cell, such as heat capacity or thermal conductivity, are required. Thus, the exothermic and endothermic nature of reversible heat generated in a complete charge/discharge cycle can be investigated. Moreover, the results of the proposed method can potentially be used to provide an additional constraint during the identification process of the equivalent circuit model parameters. The described method is applied to a 23 Ah lithium titanate cell and the corresponding results are presented.

Multiobjective Optimization Based on “Distance-to-Target” Approach of Membrane Units for Separation of CO2/CH4

The effective separation of CO2 and CH4 mixtures is essential for many applications, such as biogas upgrading, natural gas sweetening or enhanced oil recovery. Membrane separations can contribute greatly in these tasks, and innovative membrane materials are being developed for this gas separation. The aim of this work is the evaluation of the potential of two types of highly CO2-permeable membranes (modified commercial polydimethylsiloxane and non-commercial ionic liquid–chitosan composite membranes) whose selective layers possess different hydrophobic and hydrophilic characteristics for the separation of CO2/CH4 mixtures. The study of the technical performance of the selected membranes can provide a better understanding of their potentiality. The optimization of the performance of hollow fiber modules for both types of membranes was carried out by a “distance-to-target” approach that considered multiple objectives related to the purities and recovery of both gases. The results demonstrated that the ionic liquid–chitosan composite membranes improved the performance of other innovative membranes, with purity and recovery percentage values of 86 and 95%, respectively, for CO2 in the permeate stream, and 97 and 92% for CH4 in the retentate stream. The developed multiobjective optimization allowed for the determination of the optimal process design and performance parameters, such as the membrane area, pressure ratio and stage cut required to achieve maximum values for component separation in terms of purity and recovery. Since the purities and recoveries obtained were not enough to fulfill the requirements imposed on CO2 and CH4 streams to be directly valorized, the design of more complex multi-stage separation systems was also proposed by the application of this optimization methodology, which is considered as a useful tool to advance the implementation of the membrane separation processes.