Procedural Automation of Ethane Recovery to Rejection in NGL Trains

2021 ◽  
Author(s):  
Subhendu Sengupta ◽  
Vincent Goveas
Keyword(s):  
Human Error ◽  
Continuous Process ◽  
Operating Cost ◽  
Operating Efficiency ◽  
Successful Implementation ◽  
Mode Change ◽  
Batch Operation ◽  
Training Tool ◽  
Gas Processing ◽  
Start Up

Abstract This paper is based on successful implementation of procedural automation of Ethane (C2) recovery - rejection mode change using Yokogawa's Exapilot software, wherein ADNONC Gas Processing Habshan 5 & Sulphur management approved the implementation based on similar success of the Sulphur Recover Unit start-up/shutdown procedural automation & company's drive for digitalisation. Scope was to develop modules for automating C2 Recovery /Rejection change over procedure in NGL unit using M/s Yokogawa Exapilot software. These automated procedures aimed to standardize said mode change over operations by incorporating the operating know how and the expertise of skilled-experienced operators into the Exapilot system as a set of Standard Operating Procedures (SOPs) that are executed in right operating sequence for enhanced operating efficiency. Two main procedures & associated modules were designed, engineered and built using Exapilot to enable single-click change over automation for NGL units. Those were validated with operation and deployed in the Exapilot Server and were integrated with the Operator Consoles (HIS) for access, and was supplemented with operator training. Ethane Recovery to Rejection Mode Change Ethane Rejection to Recovery Mode Change Besides standardization and reduced change over time, this improved the critical asset integrity and lifespan of NGL section equipment by advocating systematic operations. Following benefits including major take away from this project: ➢ Standardized the mode change-over procedures & minimized human error by the digitalization of paper documentation procedures into electronic workflow process. Procedural Automation like Exapilot is powerful tool for digital transformation of batch/discrete operation like unit/equipment start-up/shutdown or grade/mode change over. ➢ Reduced inherent delay due to manual change over. Hence, minimizing the loss-opportunities & operating cost. Besides this tool can be used as training tool (when used in offline mode) which help operator succession plan & effective knowledge transfer ➢ Automated critical operation such as temperature/flow ramping, improved equipment integrity and prolonged equipment life. Procedural Automation using Exapilot thus can improve operation efficiency, asset integrity, equipment or material life span This paper presents a success story of procedural automation of batch operation in continuation of similar success in SRU start-up & shutdown automation. This tool along with proper integration work with DCS, has opened door for automation/digitalization in batch operation in continuous process not only in other sites of ADNOC Gas Processing and other ADNOC Group Companies but also in other industries that helps companies to enhance efficiency and fulfil their digitalization journey. Though Exapilot software belongs to M/s Yokogawa, however other DCS systems have similar software such as Honeywell DCS EPKS has E-procedure for procedural automation.

scholarly journals THE FUTURE OF PASSENGER AIR TRANSPORT – VERY LARGE AIRCRAFT AND OUT KEY HUMAN FACTORS AFFECTING THE OPERATION AND SAFETY OF PASSENGER AIR TRANSPORT

2017 ◽  
Vol 12 ◽  
pp. 104
Author(s):  
Petra Skolilova
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Operating Cost ◽  
Aircraft Design ◽  
Air Transport ◽  
Final Decision ◽  
Factors Affecting ◽  
Fuel Burn ◽  
The Future ◽  
The Impact

The article outlines some human factors affecting the operation and safety of passenger air transport given the massive increase in the use of the VLA. Decrease of the impact of the CO2 world emissions is one of the key goals for the new aircraft design. The main wave is going to reduce the burned fuel. Therefore, the eco-efficiency engines combined with reasonable economic operation of the aircraft are very important from an aviation perspective. The prediction for the year 2030 says that about 90% of people, which will use long-haul flights to fly between big cities. So, the A380 was designed exactly for this time period, with a focus on the right capacity, right operating cost and right fuel burn per seat. There is no aircraft today with better fuel burn combined with eco-efficiency per seat, than the A380. The very large aircrafts (VLAs) are the future of the commercial passenger aviation. Operating cost versus safety or CO2 emissions versus increasing automation inside the new generation aircraft. Almost 80% of the world aircraft accidents are caused by human error based on wrong action, reaction or final decision of pilots, the catastrophic failures of aircraft systems, or air traffic control errors are not so frequent. So, we are at the beginning of a new age in passenger aviation and the role of the human factor is more important than ever.

scholarly journals Solving optimal generation scheduling problem of microgrid using teaching learning based optimization algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 1632
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Demand Response ◽  
Operating Cost ◽  
Optimal Scheduling ◽  
Energy Resources ◽  
Successful Implementation ◽  
Scheduling Problem ◽  
Solar Pv ◽  
Teaching Learning Based Optimization ◽  
Diesel Generators ◽  
Teaching Learning

<p>This paper proposes a new optimal scheduling methodology for a Microgrid (MG) considering the energy resources such as diesel generators, solar photovoltaic (PV) plants, wind farms, battery energy storage systems (BESSs), electric vehicles (EVs) and demand response (DR). The penetration level of renewable and sustainable energy resources (i.e., wind, solar PV energy, geothermal and ocean energy) in power generation systems is increasing. In this work, the EVs and storage are used as flexible DR sources and they can be combined with DR to improve the flexibility of MG. Various uncertainties exist in the MGs due to the intermittent/uncertain nature of renewable energy resources (RERs) such as wind and solar PV power outputs. In this paper, these uncertainties are modeled by using the probability analysis. In this paper, the optimal scheduling problem of MG is solved by minimizing the total operating cost (TOC) of MG. The TOC minimization objective is formulated by considering the cost due to power exchange between main grid and MG, diesel generators, wind, solar PV units, EVs, BESSs, and DR. The successful implementation of optimal scheduling of MG requires the widespread use of demand response and EVs. In this paper, teaching-learning-based optimization (TLBO) algorithm is used to solve the proposed optimization problem. The simulation studies are performed on a test MG by considering all the components of MG.</p>

scholarly journals An Automated System To Replace A Human User In Conventional Laboratory Well Plate Imaging

2017 ◽  
Author(s):  
Connor Verheyen ◽  
Cornelis Rowaan ◽  
Bryan Gatto ◽  
Daniel Gizachew
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Human Error ◽  
Imaging System ◽  
Automated System ◽  
Prototype System ◽  
Successful Implementation ◽  
Two Dimensional ◽  
Image Capture ◽  
Automated Calibration

We here developed an automated well plate imaging system to eliminate the requirement for continuous human operation, thus freeing up the valuable time of a scientific researcher and removing the possibility of fatigue-induced human error. Specifically, we created a prototype system with programmed two-dimensional movement, automated calibration, variable plate configuration compatibility, variable path feasibility, reliable well plate image capture, and an intuitive graphical user interface. Successful implementation of our device would immediately benefit laboratory scientists, giving them more time to pursue the next biomedical breakthroughs.

Modelling the barriers of Lean Six Sigma for Indian micro-small medium enterprises

2019 ◽  
Vol 31 (5) ◽  
pp. 673-695 ◽  
Author(s):  
Mahipal Singh ◽  
Pankaj Kumar ◽  
Rajeev Rathi
Keyword(s):  
Statistical Analysis ◽  
Six Sigma ◽  
Human Error ◽  
Practical Implication ◽  
Pairwise Comparison ◽  
Lean Six Sigma ◽  
Successful Implementation ◽  
Pairwise Comparison Matrix ◽  
Content Type ◽  
Small Medium Enterprises

Purpose The purpose of this paper is to investigate the barriers of Lean Six Sigma (LSS) and develop the interrelationship among them using interpretive structural modelling (ISM) and Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC). Design/methodology/approach Using systematic literature review and expert’s opinions, 26 LSS barriers have been extracted and finalized through statistical analysis, that is importance-index analysis and corrected item minus total correlation methods. The statistical analysis of purified 22 LSS barriers has been carried out and consistency of finalized barriers has been checked through reliability statistical test in Statistical Package for the Social Sciences software. Finally, the contextual relationship among finalized LSS barriers is developed using ISM and MICMAC approach. Findings The ISM model indicates that insufficient management commitment and involvement, lack of resources, lack of training and education, lack of strategic thinking, lack of training funds are strategic factors; improper project selection, poor selection of employee for belt training, lack of total employee involvement, lack of awareness of about LSS are prudent factors; unclear vision, high implementation cost, resistance to culture change, weak supplier linkage, poor alignment between company’s goal and customer demand are burst factors. Furthermore, MICMAC analysis is splitting the LSSBs in four clusters according to their driving power and dependency. These results provide a clear mind-set to engineering manager for focusing more on LSS barriers according to their driving power and dependency. Research limitations/implications There may be biasness in making pairwise comparison matrix of barriers due to involvement of expert’s opinion as human error. Practical implications The outcome of this paper provides robust practical implication for LSS researchers and practitioners. The researcher and practitioners must consciously concentrate on the identified LSSBs more conventionally during LSS implementation, and they need to plan strategically to avoid any implementation failure. Originality/value For successful implementation of LSS in any organization, it is necessary and permeable to make strategy for controlling LSS barriers at initial stage. So this paper is a leading attempt to highlight main LSS barriers and interrelate them using ISM and MICMAC approach. It provides a clear path for tackling LSS barriers to engineering managers, researchers and consultants.

scholarly journals Schedule-Based Passenger Assignment for High-Speed Rail Networks considering the Ticket-Booking Process

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Huanyin Su ◽  
Feng Shi ◽  
Guangming Xu ◽  
Jin Qin ◽  
Xinghua Shan
Keyword(s):  
High Speed ◽  
Continuous Process ◽  
Operating Efficiency ◽  
High Speed Rail ◽  
Rail Network ◽  
Assignment Method ◽  
Rail Systems ◽  
Improvement Measures ◽  
Passenger Assignment ◽  
Partition Interval

This paper proposes a schedule-based passenger assignment method for high-speed rail networks considering the ticket-booking process. Passengers book tickets to reserve seats during the presale period in high-speed rail systems and passengers on trains are determined during the ticket-booking process. The ticket-booking process is modeled as a continuous and deterministic predecision process. A solution algorithm is designed using the discretization of the continuous process by partitioning the ticket-booking time and the optimal paths remain constant in any partition interval. Finally, an application to the Chinese high-speed rail network is presented. A comparison of the numerical results with the reality is conducted to validate the efficiency and precision of the method and algorithm. Based on the results, the operating efficiency of the current train schedule is evaluated and some specific improvement measures are proposed.

scholarly journals Patients and hospital managers want laparoscopic simulation training to become mandatory before live operating: a multicentre qualitative study of stakeholder perceptions

2018 ◽  
Vol 5 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jessica Preshaw ◽  
Dimitrios Siassakos ◽  
Mark James ◽  
Timothy Draycott ◽  
Sanjay Vyas ◽  
...  
Keyword(s):  
Patient Safety ◽  
Laparoscopic Surgery ◽  
Qualitative Study ◽  
Human Error ◽  
Simulation Training ◽  
Surgical Simulation ◽  
Successful Implementation ◽  
Hospital Managers ◽  
Initial Learning ◽  
Competency Based

BackgroundSurgical procedures are complex and susceptible to human error. Individual surgical skill correlates with improved patient outcomes demonstrating that surgical proficiency is vitally important for patient safety. Evidence demonstrates that simulation training improves laparoscopic surgical skills; however, projects to implement and integrate laparoscopic simulation into core surgical curricula have had varied success. One barrier to successful implementation has been the lack of awareness and prioritisation of simulation initiatives by key stakeholders.ObjectiveTo determine the knowledge and perceptions of patients and hospital managers on laparoscopic surgery and simulation training in patient safety and healthcare.MethodA qualitative study was conducted in the Southwest of England. 40 semistructured interviews were undertaken with patients attending general gynaecology clinics and general surgical and gynaecology hospital managers.ResultsSix key themes identified included: positive expectations of laparoscopic surgery; perceptions of problems and financial implications of laparoscopic surgery; lack of awareness of difficulties with surgical training; desire for laparoscopic simulation training and competency testing for patient benefit; conflicting priorities of laparoscopic simulation in healthcare; and drawbacks of surgical simulation training. Patients and managers were largely unaware of the risks of laparoscopic surgery and challenges for training. Managers highlighted conflicting financial priorities when purchasing educational equipment. Patients stated that they would have greater confidence in a surgeon who had undertaken mandatory surgical simulation training and perceived purchasing simulation equipment to be a high priority in the National Health Services. Most patients and hospital managers believed trainees should pass an examination on a simulator prior to live operating.ConclusionsCompetency-based mandatory laparoscopic simulation was strongly supported by the majority of stakeholders to augment the initial learning curve of surgeons.

Solved upscaling problems for implementing deammonification of rejection water

2006 ◽  
Vol 53 (12) ◽  
pp. 121-128 ◽  
Author(s):  
B. Wett
Keyword(s):  
Nitrogen Removal ◽  
Metabolic Pathways ◽  
Energy Savings ◽  
Scientific Literature ◽  
Compressed Air ◽  
Successful Implementation ◽  
Design Capacity ◽  
Autotrophic Metabolism ◽  
Start Up ◽  
The Mean

So far, extremely efficient metabolic pathways for nitrogen removal exclusively by autotrophic organisms are well established in scientific literature but not in practice. This paper presents results from the successful implementation of rejection water deammonification in a full-scale single sludge system at the WWTP Strass, Austria. Anaerobic ammonia oxidising biomass has been accumulated during a 2.5 year start-up period when the reactor size was gradually scaled up in the steps. The pH-controlled deammonification system (DEMON) has reached a design capacity of eliminating approximately 300 kg of nitrogen per day. Energy savings outperform expectations, decreasing the mean specific demand for compressed air from 109 m3(kg N)−1 to 29 m3(kg N)−1. Dominance of autotrophic metabolism is confirmed by organic effluent loads topping influent loads.

scholarly journals A Two-Stage Configuration Method of Integrated Community Energy System Considering Demand Response

2021 ◽  
Vol 252 ◽  
pp. 03011
Author(s):  
Jianfeng Yang ◽  
Tianxiang Xie ◽  
Chang Zhang ◽  
Jie Dong ◽  
Jianhao Zhang ◽  
...  
Keyword(s):  
Demand Response ◽  
Operating Cost ◽  
Energy System ◽  
Operating Efficiency ◽  
Maintenance Cost ◽  
Two Stage ◽  
Second Stage ◽  
Low Emission ◽  
Investment Cost ◽  
Annual Operating Cost

The integrated community energy system (ICES) has aroused considerable attention for its low emission and high operating efficiency. The existing configuration methods for ICES with multi-energy sectors ignored the controllable load. In this paper, a two-stage configuration method of ICES is developed to achieve the minimum annual investing and operating cost. At the first stage, the capacities of components in ICES are optimized to minimize the annual investment cost of ICES. At the second stage, the annual operating cost including the electricity and gas purchase costs and the component maintenance cost is minimized to satisfy the energy load. The controllable load under the time-of-use energy price in seasonal typical days is considered in the second stage. Relevant simulations are conducted to validate the effectiveness of the proposed configuration method for ICES. Considering the controllable load, comparative simulations illustrate that the proposed configuration method can significantly reduce the battery investment cost.

scholarly journals A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5658
Author(s):  
Panayiotis Theodoropoulos ◽  
Christos C. Spandonidis ◽  
Fotis Giannopoulos ◽  
Spilios Fassois
Keyword(s):  
Condition Monitoring ◽  
Continuous Process ◽  
Operating Cost ◽  
Essential Element ◽  
Cost Savings ◽  
Process Improvements ◽  
Detection Model ◽  
Efficiency Increase ◽  
Marine Engineering ◽  
The Impact

The ability to exploit data for obtaining useful and actionable information and for providing insights is an essential element for continuous process improvements. Recognizing the value of data as an asset, marine engineering puts data considerations at the core of system design. Used wisely, data can help the shipping sector to achieve operating cost savings and efficiency increase, higher safety, wellness of crew rates, and enhanced environmental protection and security of assets. The main goal of this study is to develop a methodology able to harmonize data collected from various sensors onboard and to implement a scalable and responsible artificial intelligence framework, to recognize patterns that indicate early signs of defective behavior in the operational state of the vessel. Specifically, the methodology examined in the present study is based on a 1D Convolutional Neural Network (CNN) being fed time series directly from the available dataset. For this endeavor, the dataset undergoes a preprocessing procedure. Aspiring to determine the effect of the parameters composing the networks and the values that ensure the best performance, a parametric inquiry is presented, determining the impact of the input period and the degree of degradation that our models identify adequately. The results provide an insightful picture of the applicability of 1D-CNN models in performing condition monitoring in ships, which is not thoroughly examined in the maritime sector for condition monitoring. The data modeling along with the development of the neural networks was undertaken with the Python programming language.

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