جستجوی مقالات مرتبط با کلیدواژه "پیش بینی" در نشریات گروه "مهندسی شیمی، نفت و پلیمر"

Time and cost are of great important issues in each project. Therefore, in the drilling industry as well, which is one of the most costly industries, there should be taken appropriate measures to save time and cost. In drilling, the operation can be carried out at a lower cost and time, by choosing the appropriate tool, as well as predicting accurate and timely parameters and probable problems. In this regard, one of the most effective strategies is to investigate field data and subsequently development and improvement of tools for the achieved results. In the drilling industry, in order to identify the problem and improve the drilling performance, generally, either laboratory tests or empirical models are used in accordance with previous experience. In this research, we tried to use novel methods and intelligent modeling to predict the penetration rate in drilling operations. For this purpose, artificial neural network (ANN) approach was used. In this paper, using an artificial neural network and data from Shadegan oil field drilling data (400 data), a model was developed to predict the rate of penetration, and then the results were compared with the results of modeling with multivariate regression. To evaluate the main characteristics of the model, its accuracy was measured using adjacent wells data. The results of the research showed that the correlation coefficient for the neural network is 0.97, and determination coefficient is 0.94 and the error histogram rate is proportional to the zero error 0.005, while the corresponding value in the statistical analysis the correlation coefficient is 0.94 and the determination coefficient is equal to 0.89, which indicates a higher accuracy of neural network modeling.

The porosity and pore size of polystyrene (PS) porous films prepared by immersion precipitation can be adjusted by varying the composition of the ingredients. The effect of composition variations on the characteristics of porous films can be elucidated by thermodynamic parameters.

A PS/chloroform solution with additives (polystyrene-block-poly(acrylic acid), polyethylene glycol (PEG), and glycerol) and/or non-solvent (2-propanol) was prepared. Then, the solution was cast and immersed in a 2-propanol/chloroform coagulation bath. The resultant porous film was imaged by scanning electron microscopy (SEM) and investigated by image analysis. The phase diagram of system was plotted through experimental measurements (cloud point) and theoretical method. The thermodynamic properties of the solution and coagulation bath were evaluated using non-solvent osmotic pressure difference (ΔΠNS) and solvent osmotic pressure difference (ΔΠNS) between the solution and coagulation bath. Moreover, the approaching ratio of solution (ARSolution) and bath (ARBath) to the binodal curve was also obtained.

The porosity and pore size of the films increased with decreasing the polymer concentration and adding the additives to the casting solution. By decreasing polymer concentration from 17% wt to 12% wt, the film structure changed from dense to porous (35% porosity and average pore diameter 2.8 μm). Addition of small amounts of PEG400 and glycerol to the solution (20% by weight relative to PS) significantly increased the porosity of the films from 35% to 54% and 66%, respectively. The porosity and pore size decreased first and then increased with adding non-solvent to the solution. Adding solvent to the bath helps increase porosity. Addition of non-solvent to the solution and addition of solvent to the bath were accompanied by decreasing solvent quality (ARSolution increase) and decreasing precipitation power of coagulant (ARBath increase) for the polymer, respectively. The increase in porosity and pore size was mainly associated with increasing ΔΠNS and decreasing ΔΠS.

Predicting corporate financial distress is one of the main issues facing managers, which can significantly contribute to companies' success and survival because corporate managers can be informed of financial distress and bankruptcy by providing warning and timely signals. Therefore, it is possible to avoid wasting resources and damages due to bankruptcy through proper management. This study's main purpose was to select the financial variables affecting the prediction of financial distress of companies listed on the Tehran Stock Exchange and then to predict financial distress using the ANN method. Therefore, first, 106 companies were selected by the simple random sampling method. Their financial data were extracted during 2007-2020. The relationship between variables was examined through the Pearson correlation test. Out of 34 financial ratios, 24 ratios with a significant relationship were selected. Finally, the ANN method was used to predict corporate financial distress, in which the financial distressfulness or non-distressfulness of 103 companies was correctly predicted. Comparing the prediction made by the ANN method with the actual values of the dependent variable in 2020, it was found that this method correctly predicted the corporate financial distress with a confidence level of 97%.

The prediction of drilling rate is one of the important issue because of its role in minimizing drilling costs to optimize the drilling process. Field data analysis is a key element in reducing costs and improving drilling operations. Furthermore, developing field information analysis tools and providing prediction modelsare two alternatives to improve drilling operations. When a drilling system is deployed, there are only a few limited parameters which can be controlled and changed. In general, the main purpose of this research is to apply intelligent techniques and provide graphical tools for predicting drilling performance. For this purpose, a database of field data such as well depth, drill weight, drill speed, drill chuck, weight on the hook and the torque was established from one of the southern fields of Iran. In this research, two different types of graphical tools were proposed to predict the drilling rate of penetration as well as to calculate the cost per foot, using a fuzzy neural network and Neuro-fuzzy approaches. The goals of the economic evaluation are the drill performance and the cost-per-foot calculation. The results showed that a good correlation coefficient (R2=0.94) was obtained to predict the penetration rate using the neural network. In order to improve the findings, the fuzzy neural network method was applied. The results demonstrated that a very good relationship with high precision having a coefficient of determination (R2=0.99) was obtained and thereby it depicted a significant improvement in the accuracy of the prediction models.

The population balance methods are employed to model sulfur vulcanization process of natural rubber (NR) compounds. The proposed primary models are so far empirical and/ or mechanistic and have various limitations that do not embody all reactions and formation of all the process products. In this work، the models are developed، using population balance methods that explicitly acknowledge the nature of all the formed species and the various types of reactions. The kinetic model can also accurately describe the complete cure responses including the scorch delay، cure time and the reversion for a wide range of compositions، using a single set of rate constants. In addition، the concentration profiles of all the reaction intermediates as a function of polysulfidic lengths are predicted. The population balance model provides a quantitative framework for explicitly incorporating mechanistically reasonable chemistry of the vulcanization process of different natural rubber compounds.

The changes in the behaviour of mechanical properties of low density polyethylene-thermoplastic corn starch (LDPE-TPCS) nanocomposites were studied by an adaptive neuro-fuzzy interference system. LDPE-TPCS composites containing different quantities of nanoclay (Cloisite®15A، 0. 5-3wt%) were prepared by extrusion process. In practice، it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and mechanical properties of the nanocomposites. In this paper، an adaptive neuro-fuzzy inference system (ANFIS) was used for non-linear mapping between the processing parameters and the mechanical properties of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks، could be used as a multiple inputs-multiple outputs to predict mechanical properties (such as ultimate tensile strength، elongation-at-break، Young’s modulus and relative impact strength) of the nanocomposites. The proposed ANFIS model utilizes temperature، torque and Cloisite®15A contents as input parameters to predict the desired mechanical properties. The results obtained in this work indicated that ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values > 0. 8) between the experimental and simulated outputs.

This paper is a review on researches considering theoretical prediction of polymeric membrane morphology prepared via phase inversion. Membrane morphology possesses a significant effect on efficiency. Accordingly, the knowledge of membrane morphology and influence on membrane performance is vital. Controlling membrane preparation conditions results in a desired morphology leading to an appropriate performance. In this review paper firstly, current procedures for membrane fabrication via phase inversion were briefly discussed. This was followed by tackling the papers discussing the prediction of membrane morphology on the basis of phase diagram. Finally, Flory – Huggins theory, which is a useful tool for prediction of ternary phase diagrams, was explained. Moreover, the recently published Compressible Regular Solution (CRS) model was mentioned.