Evolving simple-to-apply models for estimating thermal conductivity of supercritical CO2

Today, due to extensive applications of supercritical fluids technology in various chemical engineering process and industrial fields, predicting thermal conductivity of supercritical carbon dioxide is vital. In this research, two simple-to-apply models have been developed to estimate thermal conductivity of supercritical CO₂ as a function of temperature, pressure and density over broad ranges. This research presents a predictive tool based on LSSVM to predict thermal conductivity of supercritical CO₂. Genetic algorithm is employed to determine hyper-variables which are included in the LSSVM approach. In this regard, a set of accessible data containing 745 data points has been gathered from the previous published papers. Estimations are found to be in excellent agreement with reported data. Moreover, statistical analyses have been applied to evaluate the performance of two models. The obtained values of Mean Squared Error and R-Square were 7.415866, 0.9935 and 0.046527, 1.00 for the correlation and LSSVM model, respectively. The developed tools can be of immense practical value for chemical engineers to have a quick check of thermal conductivity of supercritical CO₂ at an extensive range of conditions.     

Parametric Optimization of Pulsed Current Gas Arc Welding of Dissimilar Welding Between UNS32750 and AISI 321 Based on Taguchi Method

In this research, Taguchi method (DOE technique) was used to optimize welding parameters in a dissimilar joint between the duplex stainless steel UNS32750 and the austenite stainless steel AISI 321 for the corrosion resistance. For joining, pulsed current gas arc welding with the ER2594 electrode was used. Pulse current, background current, % on time and current frequency were considered in three levels. Welding conditions were based on the L₉ Taguchi’s orthogonal array design of experiment. Signal-to-noise ratio was used to estimate the optimal conditions. The parameters and levels were considered as the optimal conditions in which the higher pitting potential could be obtained. Analysis of variance was carried out to determine the influential effect of each parameter. The pitting potential was evaluated by the potentiodynamic polarization test in the 3.5% NaCl solution at room temperature. Microstructural characterization and mechanical properties of the optimal joint were determined by optical microscopy and Vickers microhardness, respectively. According to the results of Taguchi method, the pulse current of 130 A, the Background current of 60 A, the % on time of 90% and the current frequency of 3 Hz were obtained as the optimal conditions. Pitting potential under optimal conditions (1.03 V) was close to Taguchi prediction (0.99 V). Analysis of Variance also indicated that the most effective parameter on the pitting corrosion was Background current. The percentage contributions of pulse current, background current, current frequency and % on time were 38.01, 32.48, 22.85 and 6.6%, respectively. The results showed that adjusting heat input led to the equal austenite-ferrite proportion.     

Predicting a model for agile NPD process with fuzzy cognitive map: the case of Iranian manufacturing enterprises

Successful development of the new product (NPD) is one of the most critical tasks in manufacturing process and challenging issues that managers usually face. In response to unanticipated changes in volatile markets, agile NPD is a focus to manufacturing companies. Identifying the causal relationships between the main factors of agile NPD process as a complex system, can influence the effectiveness of managers’ decision making, and the productivity of actions in this kind of process. This study reports the results of an empirical research done on the main factors affecting the agile NPD process and the relationships between them in Iranian manufacturing enterprises. These factors are identified and categorized in six main groups by explanatory factor analysis method, and the relationships between them are introduced with the simple fuzzy cognitive map method.     

Evaluating scholars based on their academic collaboration activities: two indices, the RC-index and the CC-index, for quantifying collaboration activities of researchers and scientific communities

Although there are many studies for quantifying the academic performance of researchers, such as measuring the scientific performance based on the number of publications, there are no studies about quantifying the collaboration activities of researchers. This study addresses this shortcoming. Based on three measures, namely the collaboration network structure of researchers, the number of collaborations with other researchers, and the productivity index of co-authors, two new indices, the RC-Index and CC-Index, are proposed for quantifying the collaboration activities of researchers and scientific communities. After applying these indices on a data set generated from publication lists of five schools of information systems, this study concludes with a discussion of the shortcomings and advantages of these indices.     

Energy use patterns and econometric models of grape production in Hamadan province of Iran

This paper examines the energy use patterns and relationship between energy input and yield for grape production in Malayer region of Hamadan Province. Data from 50 farmers were collected using a face-to-face questionnaire method. In the surveyed vineyards, average yield and energy consumption were calculated as 18,530 kgha⁻¹ and 45,213.66 MJha⁻¹, respectively. Among input energy sources, fertilizers, electricity and farmyard manure contained highest energy shares with 37.25%, 19%, and 17.84%, respectively. The energy ratio and energy productivity were found to be 4.95 and 0.42 kgMJ⁻¹.Three econometric models were developed to estimate the impact of energy inputs on yield. The results revealed that impact of chemical, fertilizer and water on yield were significant at 1% probability level. Also, indirect and non-renewable energies were found to be rather high. Sensitivity analysis indicated that among the inputs, chemical has the highest MPP value of energy inputs. RTS (returns to scale) values for grape yield was found to be 2.15; thus, there prevailed an IRS of grape for estimated model. The net return was found to be positive as 2810.56 $ha⁻¹ for grape. The benefit-cost ratio was calculated as 2.08.     

Experimental and modelling study of the solubility of CO2 in various CaCl2 solutions at different temperatures and pressures

Study of the thermodynamic behaviour of CaCl2-H2O-CO2 systems is important in different scientific areas in the chemical and petroleum engineering fields. For example, a system including salt- H20-CO2 is a common system in CO2 geological storage. During carbonate matrix acidizing, this mixture also appears as the spent acid. Hence, study of the behaviour of this system and the solubility of CO2 in CaCl2 brine in different thermodynamic conditions is critical. In this study, CO2 solubility in 0, 1.90 and 4.80 mol/L CaCl2 solutions at 328.15 to 375.15 K and 68.9 to 206.8 bar were measured. These values are normal for oil reservoirs. A popular thermodynamic model is available in the literature for estimating the CO2 solubility in pure water and NaC1 solutions. In this paper, the available model was modified by experimental work to be applicable for CaCl2 as well. Based on the measured data, the component interaction parameters in the base model were adjusted for a CaCl2-H2O-CO2 system. The developed model could predict CO2 solubility in different conditions with remarkable accuracy, particularly for high concentration solutions and at high pressures. This improvement is up to 65% better than in the base model. This model can be used in Darcy scale models for predicting wormhole propagation during carbonate matrix acidizing.     

Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller

In this paper, a hybrid method is proposed to damp frequency and power oscillations in the power system equipped with unified power flow controller (UPFC) and power system stabilizer (PSS) controllers. The method is robust with respect to operating point’s changes. This hybrid method consists of two stages: offline and online. In the offline stage, the coefficients of PSS and UPFC controllers for different operating points have been found by PSO algorithm; then in the second stage, online new fuzzy controller is proposed to select the best PSS and UPFC coefficients according to operating point. The proposed method is simulated for single machine infinite bus system-associated PSS and UPFC for three different operating points in MATLAB software, and results of proposed method simulation are investigated and compared with conventional PSS (CPSS) + UPFC, CPSS controllers. Simulation results show that the proposed method has a better performance.

     

Robust control of systems with sector nonlinearities via convex optimization: A data‐driven approach

In this paper, a new data‐driven method for designing robust controllers is proposed for systems with sector‐bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex constraints that guarantee the stability of the closed‐loop system. The main feature of the proposed approach is that only the frequency response data of the linear part of the system is used for guaranteeing the stability of the closed‐loop nonlinear system. Additionally, a convex optimization problem is formulated to ensure performance with respect to the fundamental component of a sector‐bounded nonlinearity. The case study illustrates how the proposed method can be used to control uncertain systems that are subject to sector‐bounded nonlinearities.