Design and Implementation Approach for Distributed Manufacturing Networks Using Axiomatic Design

2016 ◽  
pp. 225-250 ◽  
Author(s):  
Dominik T. Matt ◽  
Erwin Rauch
2022 ◽  
Author(s):  
Gabrijel Grubac ◽  
Joel Conrad ◽  
Peter Janiczek ◽  
Dragomir Alexandru ◽  
Sean Mcgarvey

Abstract This paper presents an analysis of the stimulation treatment design and operational efficiencies in the Black Sea. In greater detail, the paper focuses on how the stimulation design and each operational step has been optimized to save time, money and ensure an HSE driven completion methodology. An analysis was performed on the stimulation design and implementation approach looking at its evolution through a knowledge building and lesson learning process. The principal goal was to determine the most economical way to stimulate an offshore well without making any concessions to the reservoirs’ production or ultimate recovery. From the basics of well and frac design to completion optimization, effort was applied in analyzing ball launching procedures, frac spacing, logistical arrangements on the stimulation vessel and all other areas where there was potential to make improvements. Ultimately, an analysis of fluid displacements during flush was performed and deductions inferred. Past stimulation treatments were analyzed in an effort of better understanding the advantages and disadvantages in terms of production output of the wells. Similarly, an analysis of the completion approach and operational efficiencies showed the ability of pumping three stimulation stages a day. Considering that horizontal wells in the area are usually completed in six stages, a stimulation campaign would effectively be completed in 2 pumping days, 4 days total if no weather or operational delays are faced. Further improvements of this approach have been implemented in 2021 when six stimulation stages have been pumped in a single vessel ride. Applying the ball drop procedure offshore showed optimal results, as it is efficient in reducing downtime in between fracturing stages and in achieving proper isolation between stimulation zones. Likewise, with over flush being a concern throughout most of the stimulation population, certain cases in the Black Sea showed that over flushing did not adversely affect production of the wells with the production exhibiting ~15% above expected production rates post stimulation. In conclusion, the authors believe that the operational efficiencies achieved in the Black Sea are transposable in other offshore environments and successful cost cutting can be achieved by sound engineering and logistical decisions. The approach and results are beneficial in understanding where the economics are positively impacted in multistage stimulation treatments in the offshore environments, hence ultimately improving the rate of return.


2017 ◽  
Vol 117 (4) ◽  
pp. 742-753 ◽  
Author(s):  
Yaqiong Lv ◽  
Danping Lin

Purpose With the new generation Industry 4.0 coming, as well as globalization and outsourcing, products are fabricated by different parties in the distributed manufacturing network and enterprises face the challenge of consistent planning of semi-finished product in each manufacturing process in different geographical locations. The purpose of this paper is to propose a real-time operation planning system in the distributed manufacturing network to intelligently control/plan the manufacturing networks. Design/methodology/approach The feature of the proposed system is to model and simulate large distributed manufacturing networks to streamline the mechanical and production engineering processes with radio frequency identification (RFID) technology, which can keep track of process variants. To deal with concurrency and synchronization, the hierarchical timed colored Petri net (HTCPN) formalism for modeling is selected in this study. This method can help to model graphically and test the discrete events of concurrent operations. Fuzzy inference system can help for knowledge representation, so as to provide knowledge-based decision assistance in distributed manufacturing environment. Findings In this proposed system, there are two main sub-systems: one is the real-time modeling system, and the other one is intelligent operation planning system. These two systems are not parallel in the whole systems while the intelligent operation planning system should be embedded in any stage of the real-time modeling system as needed. That means real time modeling system provides the holistic structure of the studied distributed manufacturing system and realize real-time data transfer and information exchange. At the same time the embedded intelligent operation planning system fulfill operation plan function. Originality/value This new intelligent real-time operation system realizes real-time modeling with RFID-based HTCPN and smart fuzzy engine to fulfill intelligent operation planning which is highly desirable in the environment of Industry 4.0. The new intelligent manufacturing architecture will highly reduce the traditional planning workload and improve the planning results without manual error interference. The new system has been applied in a practical case to demonstrate its feasibility.


Author(s):  
N. Zainal ◽  
N. Mohamood ◽  
M. F. Norman ◽  
D. Sanmutham

<span lang="EN-US">This paper proposes a design and implementation approach of smart farming system using connected-agronomics technique for fig farm application. Nowadays, fig plants having a rapid growth in the current market demand due to its rich in natural health benefiting nutrients, antioxidants and vitamins where some farming systems have been used  in maintaining fig plant’s environmental resources to grow without fail. Smart farming is a system applied to provide user with real time information and plan for desired plant such as time intervals for watering systems. There are two major problems on maintaining the fig fruit quality; watering system fail during emergency blackout and a contagious disease known as leaf rust due to external environments. The system implements two microcontrollers, the Arduino Uno &amp; Raspberry Pi along with smartphone Android application. The system performance is evaluated based on the requirement specification, irrigation soil, surrounding temperature and moisture. It is found that all data collected by the sensors are within the optimal range of values, which are 1500 µS/cm to 1599 µS/cm for the EC reading of the fertilizer while 6.0 to 6.5 for the pH value of the soil. This prototype of smart farming was well developed and can be applied to the fig plantation environment.</span>


Sign in / Sign up

Export Citation Format

Share Document