Sunflower Oil Production Line for Sunflower Oil Plant to
- Production capacity: 100%
- Model number: DT-150
- Voltage: 220 V/380 V
- Power (W ): 22 kw/h
- Dimension (L*W*H): 48 m *12M*15M(30TPD)
- Weight: 40 tons
- Capacity : 10-500tpd
- Application: Canola oil processing equipment
- Oil rate: 18-22%
- Processing capacity: 20tpd to 200tpd
- Steam consumption: ≤280Kg/T (0.8MPa)
- Energy consumption: ≤ 15KWh/T
- Solvent consumption: ≤3Kg/T (6#solvent oil)
- Residual oil in flour: ≤1%
- Residual solvent in finished food: ≤ 300PPM (qualified detonated experiment)
De-shelled pressing-leaching technology is the key point of sunflower oil production line which avoids the negative impact of over-refining, high temperature, and acid and alkali effects on oils, so that the nutrients in the oil can be well preserved. For example, the content of vitamin E in de-shelled squeezed sunflower oil is 75.5mg/100g.
Pattern recognition based on machine learning identifies oil
- Production capacity: High
- Voltage: 220V/380V
- Dimension (L*W*H): 120*780*1100mm
- Weight: 2100kg
- Main components: Motor
- Oil type: Cooking oil
- Product name: Oil pressing machine
- Raw materials suitable: seed for cooking
- Spindle speed: 38 -46r/min
- Capacity: 30-40 Kg/h
- Screw diameter: 55 mm
- Related name: Oil presser
- Advantage: High oil yield
- Size: 1200*780*1100mm
The robustness of on-line learning applied to newly added oil types was also tested in our blind tests, where an actual groundnut oil mixed with cotton seed oil was quantified with small errors ...
INTRODUCTION TO MACHINE VISION - Assembly Mag
- Production capacity: 50-700 kg/h
- Voltage: 220/110 V
- Dimension (L*W*H): 500*180*300MM
- Weight: 15 KG
- Main components: Motor
- Function: Oilseed Press
- Material: stainless steel
- Application: for sunflower seeds, rapeseed, tea seeds, sesame seeds, peanuts, etc.
- Advantage: Low Residual Energy Saving
- Feature: High efficiency oil yield
- Keyword: Smart touch screen oil extractor
- Keyword 3: oil press machine
patterns to the actual objects (pattern matching) moving down a production line. Part location is the critical first step in the four major categories of machine vision applications. The categories are guidance, identification, gauging, and inspection, which can be remembered by the acronym (GIGI). Figure 3.
Palm Oil Processing Production Line
- Production capacity: 5TPD-100TPD
- Voltage: 220V/50HZ three-phase
- Dimension (L*W*H)): 1055*805*345mm
- Weight: 27.1 KG
- Warranty: 1 year, 1 year
- Main components: Engine, Engine
- Oil type: Oil kitchen
- Name: ini kitchen oil presser machine
- Advantage: high oil yield
- Character: easy to move
- Function: oil pressing
- Color: customer required
- Quality: high level
- Operation: Easily
- Keyword: Equipment cooking oil solvent extraction
- Model: TS-BXG-128
For small-scale palm processing farmers, Dingsheng Machine has designed single-screw and double-screw palm fruit oil press, which can process 1ton, 5 tons, 10 tons and 15 tons of palm fruit per hour. Reliable palm oil press machine with simple operation, gaining a good reputation in Africa and South America. The main products of the palm fruit ...
Supervised Machine Learning Mode for Predicting Gas-Liquid
- Production capacity: 55%-70%
- Model number: PR50B
- Voltage: 220 V
- Power (W): 3kw
- Dimension (L*W*H): 1260*1300* 850 mm
- Weight: 300 kg
- Capacity: 40-60 kg/hour
- Vacuum pump power: 370kw
- Rotation speed: 63r/min
Accurate identification of gas-liquid two-phase flow patterns during oil and gas drilling is critical to analyzing bottom hole pressure, detecting overflows in time, and preventing blowout accidents. Since the gas-liquid two-phase flow has deformable interfaces, resulting in complex gas-liquid two-phase flow patterns, the existing gas-liquid two-phase flow patterns are limited in width in ...
- How is Ai transforming oil & gas industry?
- With a foundation in digital transformation along with advancements across AI including digital twins and reinforcement learning, Oil and Gas companies are positioned to super-charge their production, maximize operational efficiency, and minimize environmental impact.
- Can machine learning predict oil production rates accurately?
- Thus, in this paper, Machine Learning (ML) techniques are presented as a robust and intelligent framework to predict oil production rates accurately and timely. The ML techniques include Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and K-nearest neighbor (KNN).
- Which ML techniques are used to predict oil production rate?
- The ML techniques include Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and K-nearest neighbor (KNN). These four techniques were engaged to predict the oil production rate of real oilfield data of 11 wells. The 11 oil wells were considered as datasets to achieve a precise prediction of the oil production rate.
- Can machine learning detect fatty acid patterns discriminative for different plant oil types?
- Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures.