با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Technical-knowledge-integrated material flow cost accounting model for energy reduction in industrial wastewater treatment
مدل حسابداری مواد مخدر فنی دانش فنی برای کاهش انرژی در درمان فاضلاب صنعتی-2021 A novel simulation model incorporating the concept of material flow cost accounting (MFCA) into a numerical
process simulator for wastewater treatment plants (WWTPs) was developed. Cost-related parameters, such as
electrical power consumption, were calculated for each unit process by referring to predetermined formulas of
design rules and technical knowledge built into the model. These calculated values were then assigned to the
outflow stream proportional to the flowrate, allowing each flow stream in the WWTP to be quantified according to
the history of assigned costs. This method increased the number of quantity centers in MFCA models regardless of
actual data availability, thus contributing complex flow configuration and flexible comparison of improvement
approaches related to financial evaluation. Energy cost allocation maps created by this model demonstrated the
benefits of anaerobic treatment in the WWTP of a soft-drink factory in Japan. Additionally in this WWTP, the
observed values of total power consumption were 40% higher than the simulated values, and improvement approaches, such as instrumental control of aeration, were evaluated for their feasibility and financial impact. These
results demonstrated the success of the model in adding and reinforcing analytical and predictive functions in the
MFCA survey method.
Keywords: Material flow cost accounting | Process simulation model | Industrial wastewater | Energy saving | Food and beverage industry |
مقاله انگلیسی |
2 |
CFD data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller
توابع شبکه عصبی مبتنی بر داده های CFD برای پیش بینی عملکرد هیدرودینامیکی یک پروانه سیکلوئید دریایی کم فشار-2020 Today, various types of propulsion systems are used in different purpose ship types. Marine cycloidal propeller
(MCP) is one of these propulsion systems, which has been designed for ships that require high maneuverability.
MCP can be considered as an especial type of marine propulsion systems, since it produces the thrust force which
is perpendicular to propeller axis of rotation. The magnitude and direction of the thrust force can be adjusted by
controlling the pitching angle of the blades, so no separate rudder is needed to manoeuvre the ship. In this study,
mathematical functions for predicting the open water hydrodynamic performance of a low-pitch MCP are presented
by training a neural network based on computational fluid dynamics (CFD) data. For this purpose, the
four nondimensional parameters of blade number (Z), ratio of blade thickness to MCP diameter (t/D), pitch (e)
and advance coefficient (λ) are considered as input variables, whereas the hydrodynamic coefficients of thrust
(Ks) and torque (Kd) are considered as targets. CFD simulations are performed for different cases of MCP with
different combinations of Z, t/D, e and λ. The results showed that a two-layer feedforward network with one
hidden layer of sigmoid neurons and at least 4 neurons in the hidden layer can be well trained by CFD data in
order to obtain functions with good accuracy in predicting Ks and Kd coefficients of a low-pitch MCP. Keywords: Marine cycloidal propeller | Hydrodynamic performance | CFD simulation | Neural network | Predictive function |
مقاله انگلیسی |