Scheduling technology either commercial or homegrown in today’s crude-oil refining industries relies on a complex simulation of scenarios where the user is solely responsible for making many different decisions manually in the search for feasible solutions over some limited time-horizon i.e., trial-and-error heuristics. As a normal outcome, schedulers abandon these solutions and then return to their simpler spreadsheet simulators due to: (i) time-consuming efforts to configure and manage numerous scheduling scenarios, and (ii) requirements of updating premises and situations that are constantly changing. Moving to solutions based in optimization rather than simulation, the lecture describes the future steps in the refactoring of the scheduling technology in PETROBRAS considering in separate the graphic user interface (GUI) and data communication developments (non-modeling related), and the modeling and process engineering related in an automated decision-making with built-in problem representation facilities and integrated data handling features among other techniques in a smart scheduling frontline.
PRESENTATION ON PLANT DESIGN FOR MANUFACTURING OF HYDROGENPriyam Jyoti Borah
Steam reforming or steam methane reforming is a method for producing syngas (hydrogen and carbon monoxide) by reaction of hydrocarbons with water. Commonly natural gas is the feedstock. The main purpose of this technology is hydrogen production.The reaction is conducted in a reformer vessel where a high pressure mixture of steam and methane are put into contact with a nickel catalyst. Catalysts with high surface-area-to-volume ratio are preferred because of diffusion limitations due to high operating temperature. Examples of catalyst shapes used are spoked wheels, gear wheels, and rings with holes. Additionally, these shapes have a low pressure drop which is advantageous for this application.
Study 2: Front-End Engineering Design and Project DefinitionGerard B. Hawkins
Study 2: Front-End Engineering Design and Project Definition
CONTENTS
2.0 PURPOSE
2.0.1 Team
2.0.2 Timing
2.0.3 Documentation
HAZARD STUDY 2: APPLICATION
2.1 Study of Process and Non-Process Activities
2.2 Study of Programmable Electronic Systems (PES)
2.3 Risk Assessment
2.4 Defining the Basis for Safe Operation
2.5 Review of Hazard Study 2
APPENDICES
Appendix A Hazard Study 2 Method
A.1 Significant Hazards Flowsheet
A.2 Event Guide Diagram
A.3 Consequence Guide Diagram
A.4 Typical Measures to Reduce Consequences
Appendix B Programmable Electronic Systems (PES) Guide Diagram
Appendix C Risk Assessment
C.1 Risk Assessment Procedure
C.2 Risk Matrix
C.3 Risk Matrix Guidance for Consequence Categories – Safety and Health Incidents
C.4 Risk Matrix Guidance for Consequence Categories – Environmental Incidents
Appendix D Key Hazards and Control Measures
Appendix E Content of Hazard Study 2 Report Package.
Course Description
This concise course is well balanced and based on more than 30 years of true project experience in Shallow and Deep Water fields around the world, from concept evaluation to first production. It is designed for Project Managers, Project Engineers, experienced or new to Subsea & highly suitable for Cost, Planning, Offshore Installation and Offshore Operation Engineers.
All the Lectures (up-dated on a regular basis) are presented with text, figures and DVDs with videos and detailed animations, used to illustrate many key points of Subsea Technologies. The objective of the course is to equip Engineers and Technicians with a good understanding on the Engineering of Subsea Production Systems (SPS) together with Umbilicals, Risers and Flowlines (SURF) required to link and operate it from
the Host.
Course Highlights
- Definition of Subsea Engineering for Field Developments / Floaters requirements Field Lay-out and System Design
- Flow Assurance Issues, Mitigation and Hydraulic Analysis
- Well Heads and Xmas Trees
- Templates, Manifolds and Subsea Hardware
- Subsea Wells Operations and Work Overs
- Inter Field Flowlines & Small Export Pipelines
- Production Riser Systems for Floaters
- Subsea Production Control Systems & Chemical Injection (including Umbilicals)
- Reliability Engineering and Risk Analysis
- Underwater Inspection, Maintenance & Repair
- New technologies for S.P.S
- 3 Major case studies for Oil, Gas and Heavy Oil Production including updates from Total, Shell and BP
Innovation of LNG Carrier-Propulsion and BOG handling technology (LNG Warring...BenedictSong1
LNG Warring State Period!
With the advantage of direct injection two-stroke MAN MEGI diesel engines and otto cycle duel fuel XDF engines, existing steam turbine-propelled LNG carriers are significantly less competitive and are in danger of survival. Shipowners will continue to make efforts to create new value by converting these steam turbine LNG carriers to FSRU, FLNG and FPU.
PRESENTATION ON PLANT DESIGN FOR MANUFACTURING OF HYDROGENPriyam Jyoti Borah
Steam reforming or steam methane reforming is a method for producing syngas (hydrogen and carbon monoxide) by reaction of hydrocarbons with water. Commonly natural gas is the feedstock. The main purpose of this technology is hydrogen production.The reaction is conducted in a reformer vessel where a high pressure mixture of steam and methane are put into contact with a nickel catalyst. Catalysts with high surface-area-to-volume ratio are preferred because of diffusion limitations due to high operating temperature. Examples of catalyst shapes used are spoked wheels, gear wheels, and rings with holes. Additionally, these shapes have a low pressure drop which is advantageous for this application.
Study 2: Front-End Engineering Design and Project DefinitionGerard B. Hawkins
Study 2: Front-End Engineering Design and Project Definition
CONTENTS
2.0 PURPOSE
2.0.1 Team
2.0.2 Timing
2.0.3 Documentation
HAZARD STUDY 2: APPLICATION
2.1 Study of Process and Non-Process Activities
2.2 Study of Programmable Electronic Systems (PES)
2.3 Risk Assessment
2.4 Defining the Basis for Safe Operation
2.5 Review of Hazard Study 2
APPENDICES
Appendix A Hazard Study 2 Method
A.1 Significant Hazards Flowsheet
A.2 Event Guide Diagram
A.3 Consequence Guide Diagram
A.4 Typical Measures to Reduce Consequences
Appendix B Programmable Electronic Systems (PES) Guide Diagram
Appendix C Risk Assessment
C.1 Risk Assessment Procedure
C.2 Risk Matrix
C.3 Risk Matrix Guidance for Consequence Categories – Safety and Health Incidents
C.4 Risk Matrix Guidance for Consequence Categories – Environmental Incidents
Appendix D Key Hazards and Control Measures
Appendix E Content of Hazard Study 2 Report Package.
Course Description
This concise course is well balanced and based on more than 30 years of true project experience in Shallow and Deep Water fields around the world, from concept evaluation to first production. It is designed for Project Managers, Project Engineers, experienced or new to Subsea & highly suitable for Cost, Planning, Offshore Installation and Offshore Operation Engineers.
All the Lectures (up-dated on a regular basis) are presented with text, figures and DVDs with videos and detailed animations, used to illustrate many key points of Subsea Technologies. The objective of the course is to equip Engineers and Technicians with a good understanding on the Engineering of Subsea Production Systems (SPS) together with Umbilicals, Risers and Flowlines (SURF) required to link and operate it from
the Host.
Course Highlights
- Definition of Subsea Engineering for Field Developments / Floaters requirements Field Lay-out and System Design
- Flow Assurance Issues, Mitigation and Hydraulic Analysis
- Well Heads and Xmas Trees
- Templates, Manifolds and Subsea Hardware
- Subsea Wells Operations and Work Overs
- Inter Field Flowlines & Small Export Pipelines
- Production Riser Systems for Floaters
- Subsea Production Control Systems & Chemical Injection (including Umbilicals)
- Reliability Engineering and Risk Analysis
- Underwater Inspection, Maintenance & Repair
- New technologies for S.P.S
- 3 Major case studies for Oil, Gas and Heavy Oil Production including updates from Total, Shell and BP
Innovation of LNG Carrier-Propulsion and BOG handling technology (LNG Warring...BenedictSong1
LNG Warring State Period!
With the advantage of direct injection two-stroke MAN MEGI diesel engines and otto cycle duel fuel XDF engines, existing steam turbine-propelled LNG carriers are significantly less competitive and are in danger of survival. Shipowners will continue to make efforts to create new value by converting these steam turbine LNG carriers to FSRU, FLNG and FPU.
Distillation Blending and Cutpoint Temperature Optimization in Scheduling Ope...Brenno Menezes
In oil refinery manufacturing, final products such as fuels, lubricants and petrochemicals are produced from crude-oil in process units considering their operations in coordination with tanks, pipelines, blenders, etc. In this process, the full range of hydrocarbon components (crude-oil) is transformed (separated, reacted, blended) into smaller boiling-point temperature ranges resulting in intermediate and final products, in which planning, scheduling and real-time optimization using distillation curves of the streams can be used to effectively model the unit-operations and predict yields and properties of their outlet streams.1 The hydrocarbon streams’ characterization or assays of both the crude-oil and its derivatives are decomposed, partitioned or characterized into several temperature cuts based on what are known as True Boiling Point (TBP) temperature distribution or distillation curves.2,3 These are one-dimensional representations of how quantity (yields) and quality (properties) data of hydrocarbon streams are distributed or profiled over its TBP temperatures where each cut is also referred to as a component, pseudocomponent or hypothetical in process simulation and optimization technology.4
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery, we proposed a new technique to optimize the blending of several streams’ distillation curves with also shifting or adjusting cutpoint temperatures of distilled streams, i.e, their initial boiling point (IBP) and final boiling point (FBP), in order to manipulate their TBP curves in either off-line or on-line environment. By shifting or adjusting the front-end and back-end of the TBP curve for one or more distillate blending streams, it allows for improved control and optimization of the final product demand quantity and quality, affording better maneuvering closer and around downstream bottlenecks such as tight property specifications and volatile demand flow and timing constrictions. This shifting or adjusting of the TBP curve’s IBP and FBP (front- and back-end respectively) ultimately requires that the unit-operation has sufficient handles or controls to allow this type of cutpoint variation where the solution from this higher-level optimization would provide set points or targets to a lower-level advanced process control systems, which are now commonplace in oil refineries.
By optimizing both the recipes of the blended material and its blending component distillation curves, very significant benefits can be achieved especially given the global push towards ultralow sulfur fuels (ULSF) due to the increase in natural gas plays reducing the demand for other oil distillates. One example is provided to highlight and demonstrate the technique.
This Training include several parts of Oil & Gas Engineering:
Petroleum Geology
Process Presentation
Utilities in an Oil & Gas Field
Process Engineering
Safety Engineering
Mechanical Engineering
Civil Engineering
Control & Instrumentation Engineering
Electrical Engineering
Design Engineering - 3D Model
Field Engineering
Commissioning & Startup
For more détails, please contact: Ramzi Fathallah
https://www.linkedin.com/in/ramzi-fathallah-a3762b85?trk=nav_responsive_tab_profile
Hydrogen recovery from purge gas(energy saving)Prem Baboo
Ammonia is continuously condensed out of the loop and fresh synthesis gas is added. Because the synthesis gas contains small quantities of methane and argon, these impurities build up in the loop and must be continuously purged to prevent them from exceeding a certain concentration. Although this purge stream can be used to supplement reformer fuel gas, it contains valuable hydrogen which is lost from the ammonia synthesis loop In order to achieve optimum conversion in synthesis convertor, it is necessary to purge a certain quantity of gas from synthesis loop so as to as to reduce inerts concentration in the loop. Purge gas stream from ammonia process contains ammonia, hydrogen, nitrogen and other inert gases. Among them, ammonia itself is the valuable product lost with the purge stream. Moreover it has a serious adverse effect on the environment.This purge gas containing about 60% Hydrogen was fully utilised as primary reformer fuel.
This presentation is an brief insight into what ATF is, its important properties, few standards followed in world in ATF Quality and ATF contamination.
Distillation Blending and Cutpoint Temperature Optimization (DBCTO) in Schedu...Brenno Menezes
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery.
To integrate blending of several streams’ distillation curves with also shifting or adjusting cutpoints of distilled streams (i.e., initial and/or final boiling-points, IBP and FBP) in order to manipulate their TBP curves in an either off- or on-line environment (Kelly et al, 2014).
Liquefied Natural Gas (LNG) Life Cycle; LNG a safe fuel? ; Quality of LNG ; Sales LNG/Gas Specifications ; NATURAL GAS VALUE CHAIN; LNG TRANSPORTATION; Global Movement of Natural Gas; Movement of Natural Gas; Movement: Pipelines and Storage; Natural Gas Infrastructure: Pipeline Systems; Types of Pipelines; Offshore Pipelines; Movement: LNG; Liquefied Natural Gas (LNG); LNG Markets (R)evolution; LIQUEFACATION; REGASIFICATION; PIPELINE NETWORK; Revolutionary LNG Technologies: FLNG and FSRU; FLOATING LNG (FLNG); FLOATING STORAGE AND REGASIFICATION (FSRU); Global Natural Gas Trade; Natural Gas Price Formation; Liberalizing Market Dynamics; Natural Gas Contracts
Get full Course here:
www.ChemicalEngineeringGuy.com/Courses
The BASIC Aspen HYSYS Course will show you how to model and simulate Processes (From Petrochemical, to Ammonia Synthesis and Polymerisation).
Analysis of Unit Operation will help you in order to optimise the Chemical Plant.
This is helpful for students, teachers, engineers and researchers in the area of R&D and Plant Design/Operation.
The course is didactic, with a lot of applied theory and Workshops/Study cases.
At the end of the course you will be able to setup a simulation, run it, get results and more important, analysis of the process for further optimization.
Chemical Engineers
Process Engineers
Students related to engineering fields
Teachers willing to learn more about process simulation
Petrochemical Engineers
This is course on Plant Simulation will show you how to setup hypothetical compounds, oil assays, blends, and petroleum characterization using the Oil Manager of Aspen HYSYS.
You will learn about:
Hypothetical Compounds (Hypos)
Estimation of hypo compound data
Models via Chemical Structure UNIFAC Component Builder
Basis conversion/cloning of existing components
Input of Petroleum Assay and Crude Oils
Typical Bulk Properties (Molar Weight, Density, Viscosity)
Distillation curves such as TBP (Total Boiling Point)
ASTM (D86, D1160, D86-D1160, D2887)
Chromatography
Light End
Oil Characterization
Using the Petroleum Assay Manager or the Oil Manager
Importing Assays: Existing Database
Creating Assays: Manually / Model
Cutting: Pseudocomponent generation
Blending of crude oils
Installing oils into Aspen HYSYS flowsheets
Getting Results (Plots, Graphs, Tables)
Property and Composition Tables
Distribution Plot (Off Gas, Light Short Run, Naphtha, Kerosene, Light Diesel, Heavy Diesel, Gasoil, Residue)
Oil Properties
Proper
Boiling Point Curves
Viscosity, Density, Molecular Weight Curves
This is helpful for students, teachers, engineers and researchers in the area of R&D, specially those in the Oil and Gas or Petroleum Refining industry.
This is a "workshop-based" course, there is about 25% theory and about 75% work!
At the end of the course you will be able to handle crude oils for your fractionation, refining, petrochemical process simulations!
Join Postgres experts Marc Linster and Devrim Gündüz as they provide a step by step guide for installing PostgreSQL and EDB Postgres Advanced Server on Linux.
Highlights include:
- The advantages of native packages
- An in-depth look at RPMs and DEBs
- A step-by-step demo
This presentation describes the considerations involved in selecting the shell and tube exchanger according to TEMA Designations. Also, it helps to identify whether fluid should be sent tube side or shell side
Distillation Blending and Cutpoint Temperature Optimization in Scheduling Ope...Brenno Menezes
In oil refinery manufacturing, final products such as fuels, lubricants and petrochemicals are produced from crude-oil in process units considering their operations in coordination with tanks, pipelines, blenders, etc. In this process, the full range of hydrocarbon components (crude-oil) is transformed (separated, reacted, blended) into smaller boiling-point temperature ranges resulting in intermediate and final products, in which planning, scheduling and real-time optimization using distillation curves of the streams can be used to effectively model the unit-operations and predict yields and properties of their outlet streams.1 The hydrocarbon streams’ characterization or assays of both the crude-oil and its derivatives are decomposed, partitioned or characterized into several temperature cuts based on what are known as True Boiling Point (TBP) temperature distribution or distillation curves.2,3 These are one-dimensional representations of how quantity (yields) and quality (properties) data of hydrocarbon streams are distributed or profiled over its TBP temperatures where each cut is also referred to as a component, pseudocomponent or hypothetical in process simulation and optimization technology.4
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery, we proposed a new technique to optimize the blending of several streams’ distillation curves with also shifting or adjusting cutpoint temperatures of distilled streams, i.e, their initial boiling point (IBP) and final boiling point (FBP), in order to manipulate their TBP curves in either off-line or on-line environment. By shifting or adjusting the front-end and back-end of the TBP curve for one or more distillate blending streams, it allows for improved control and optimization of the final product demand quantity and quality, affording better maneuvering closer and around downstream bottlenecks such as tight property specifications and volatile demand flow and timing constrictions. This shifting or adjusting of the TBP curve’s IBP and FBP (front- and back-end respectively) ultimately requires that the unit-operation has sufficient handles or controls to allow this type of cutpoint variation where the solution from this higher-level optimization would provide set points or targets to a lower-level advanced process control systems, which are now commonplace in oil refineries.
By optimizing both the recipes of the blended material and its blending component distillation curves, very significant benefits can be achieved especially given the global push towards ultralow sulfur fuels (ULSF) due to the increase in natural gas plays reducing the demand for other oil distillates. One example is provided to highlight and demonstrate the technique.
This Training include several parts of Oil & Gas Engineering:
Petroleum Geology
Process Presentation
Utilities in an Oil & Gas Field
Process Engineering
Safety Engineering
Mechanical Engineering
Civil Engineering
Control & Instrumentation Engineering
Electrical Engineering
Design Engineering - 3D Model
Field Engineering
Commissioning & Startup
For more détails, please contact: Ramzi Fathallah
https://www.linkedin.com/in/ramzi-fathallah-a3762b85?trk=nav_responsive_tab_profile
Hydrogen recovery from purge gas(energy saving)Prem Baboo
Ammonia is continuously condensed out of the loop and fresh synthesis gas is added. Because the synthesis gas contains small quantities of methane and argon, these impurities build up in the loop and must be continuously purged to prevent them from exceeding a certain concentration. Although this purge stream can be used to supplement reformer fuel gas, it contains valuable hydrogen which is lost from the ammonia synthesis loop In order to achieve optimum conversion in synthesis convertor, it is necessary to purge a certain quantity of gas from synthesis loop so as to as to reduce inerts concentration in the loop. Purge gas stream from ammonia process contains ammonia, hydrogen, nitrogen and other inert gases. Among them, ammonia itself is the valuable product lost with the purge stream. Moreover it has a serious adverse effect on the environment.This purge gas containing about 60% Hydrogen was fully utilised as primary reformer fuel.
This presentation is an brief insight into what ATF is, its important properties, few standards followed in world in ATF Quality and ATF contamination.
Distillation Blending and Cutpoint Temperature Optimization (DBCTO) in Schedu...Brenno Menezes
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery.
To integrate blending of several streams’ distillation curves with also shifting or adjusting cutpoints of distilled streams (i.e., initial and/or final boiling-points, IBP and FBP) in order to manipulate their TBP curves in an either off- or on-line environment (Kelly et al, 2014).
Liquefied Natural Gas (LNG) Life Cycle; LNG a safe fuel? ; Quality of LNG ; Sales LNG/Gas Specifications ; NATURAL GAS VALUE CHAIN; LNG TRANSPORTATION; Global Movement of Natural Gas; Movement of Natural Gas; Movement: Pipelines and Storage; Natural Gas Infrastructure: Pipeline Systems; Types of Pipelines; Offshore Pipelines; Movement: LNG; Liquefied Natural Gas (LNG); LNG Markets (R)evolution; LIQUEFACATION; REGASIFICATION; PIPELINE NETWORK; Revolutionary LNG Technologies: FLNG and FSRU; FLOATING LNG (FLNG); FLOATING STORAGE AND REGASIFICATION (FSRU); Global Natural Gas Trade; Natural Gas Price Formation; Liberalizing Market Dynamics; Natural Gas Contracts
Get full Course here:
www.ChemicalEngineeringGuy.com/Courses
The BASIC Aspen HYSYS Course will show you how to model and simulate Processes (From Petrochemical, to Ammonia Synthesis and Polymerisation).
Analysis of Unit Operation will help you in order to optimise the Chemical Plant.
This is helpful for students, teachers, engineers and researchers in the area of R&D and Plant Design/Operation.
The course is didactic, with a lot of applied theory and Workshops/Study cases.
At the end of the course you will be able to setup a simulation, run it, get results and more important, analysis of the process for further optimization.
Chemical Engineers
Process Engineers
Students related to engineering fields
Teachers willing to learn more about process simulation
Petrochemical Engineers
This is course on Plant Simulation will show you how to setup hypothetical compounds, oil assays, blends, and petroleum characterization using the Oil Manager of Aspen HYSYS.
You will learn about:
Hypothetical Compounds (Hypos)
Estimation of hypo compound data
Models via Chemical Structure UNIFAC Component Builder
Basis conversion/cloning of existing components
Input of Petroleum Assay and Crude Oils
Typical Bulk Properties (Molar Weight, Density, Viscosity)
Distillation curves such as TBP (Total Boiling Point)
ASTM (D86, D1160, D86-D1160, D2887)
Chromatography
Light End
Oil Characterization
Using the Petroleum Assay Manager or the Oil Manager
Importing Assays: Existing Database
Creating Assays: Manually / Model
Cutting: Pseudocomponent generation
Blending of crude oils
Installing oils into Aspen HYSYS flowsheets
Getting Results (Plots, Graphs, Tables)
Property and Composition Tables
Distribution Plot (Off Gas, Light Short Run, Naphtha, Kerosene, Light Diesel, Heavy Diesel, Gasoil, Residue)
Oil Properties
Proper
Boiling Point Curves
Viscosity, Density, Molecular Weight Curves
This is helpful for students, teachers, engineers and researchers in the area of R&D, specially those in the Oil and Gas or Petroleum Refining industry.
This is a "workshop-based" course, there is about 25% theory and about 75% work!
At the end of the course you will be able to handle crude oils for your fractionation, refining, petrochemical process simulations!
Join Postgres experts Marc Linster and Devrim Gündüz as they provide a step by step guide for installing PostgreSQL and EDB Postgres Advanced Server on Linux.
Highlights include:
- The advantages of native packages
- An in-depth look at RPMs and DEBs
- A step-by-step demo
This presentation describes the considerations involved in selecting the shell and tube exchanger according to TEMA Designations. Also, it helps to identify whether fluid should be sent tube side or shell side
Steel building Graded Unit Civil Engineering Project HND Project Glasgow Kel...Tehmas Saeed
This was my second Graded unit Project, it involved construction of Steel framed Office building, based on HND modules we were advised to devise solution of Steel building which two areas of specialisation in which I chose Sustainability and Frame Structure. This assignment does not have drawings and calculations unfortunately i have lost them, however their is a copy at Former Stow College now Glasgow Kelvin college so students can access from there. For any structural help, I would strongly advise to meet Mr Murdo a very competent lecturer in Kelvin college.
Although its rough guide, we were not heavily using journals at that stage, as we relied mostly on our course material. However some of the Green material which i used was taken from companies publications.
Crude-Oil Blend Scheduling Optimization: An Application with Multi-Million D...Alkis Vazacopoulos
The economic and operability benefits associated with better crude-oil blend scheduling are numerous and significant. The crude-oils that arrive at the oil-refinery to be processed into the various refined-oils must be carefully handled and mixed before they are charged to the atmospheric and vacuum distillation unit or pipestill. The intent of this article is to highlight the importance and details of optimizing the scheduling of an oil-refinery’s crude-oil feedstocks from the receipt to the charging of the pipestills.
CRUDE-OIL BLEND SCHEDULING OPTIMIZATION OF AN INDUSTRIAL-SIZED REFINERY: A DI...Brenno Menezes
We propose a discrete-time formulation for optimization of scheduling in crude-oil refineries considering both the logistics details practiced in industry and the process feed diet and quality calculations. The quantity-logic-quality phenomena (QLQP) involving a non-convex mixed-integer nonlinear (MINLP) problem is decomposed considering first the logistics model containing quantity and logic variables and constraints in a mixed-integer linear (MILP) formulation and, secondly, the quality problem with quantity and quality variables and constraints in a nonlinear programming (NLP) model by fixing the logic results from the logistics problem. Then, stream yields of crude distillation units (CDU), for the feed tank composition found in the quality calculation, are updated iteratively in the following logistics problem until their convergence is achieved. Both local and global MILP results of the logistics model are solved in the NLP programs of the quality and an ad-hoc criteria selects to continue those among a score of the MILP+NLP pairs of solutions. A pre-scheduling reduction to cluster similar quality crude-oils decreases the discrete search space in the possible superstructure of the industrial-sized example that demonstrates our tailor-made decomposition scheme of around 3% gap between the MILP and NLP solutions.
OpenPOWER Webinar from University of Delaware - Title :OpenMP (offloading) o...Ganesan Narayanasamy
This presentation discusses the on-going project on building a validation and verification (V&V) testsuite of the widely popular directive-based parallel programming model, OpenMP. The talk will present results of the OpenMP offloading features implemented in various compilers targeting Summit among other systems. This project is open-source and the SOLLVE V&V team welcomes collaborations.
This is 101 introduction of GPORCA for Open Source developers. GPORCA is open source query optimizer for SQL on MPP (massive parallel processing) database system like Greenplum. You can find the overview of GPORCA, as well as how to debug and contribute back to OSS community.
Data Science at Scale on MPP databases - Use Cases & Open Source ToolsEsther Vasiete
Pivotal workshop slide deck for Structure Data 2016 held in San Francisco.
Abstract:
Learn how data scientists at Pivotal build machine learning models at massive scale on open source MPP databases like Greenplum and HAWQ (under Apache incubation) using in-database machine learning libraries like MADlib (under Apache incubation) and procedural languages like PL/Python and PL/R to take full advantage of the rich set of libraries in the open source community. This workshop will walk you through use cases in text analytics and image processing on MPP.
ADMM-Based Scalable Machine Learning on Apache Spark with Sauptik Dhar and Mo...Databricks
Apache Spark is rapidly becoming the de facto framework for big-data analytics. Spark’s built-in, large-scale Machine Learning Library (MLlib) uses traditional stochastic gradient descent (SGD) to solve standard ML algorithms. However, MlLib currently provides limited coverage of ML algorithms. Further, the convergence of the adopted SGD approach is heavily dictated by issues such as step-size selection, conditioning of the problem and so on, making it difficult for adoption by non-expert end users.
In this session, the speakers introduce a large-scale ML tool built on the Alternating Direction Method of Multipliers (ADMM) on Spark to solve a gamut of ML algorithms. The proposed approach decomposes most ML problems into smaller sub-problems suitable for distributed computation in Spark.
Learn how this toolkit provides a wider range of ML algorithms, better accuracy compared to MLlib, robust convergence criteria and a simple python API suitable for data scientists – making it easy for end users to develop advanced ML algorithms at scale, without worrying about the underlying intricacies of the optimization solver. It’s a useful arsenal for data scientists’ ML ecosystem on Spark.
EclipseCon Eu 2015 - Breathe life into your Designer!melbats
You have your shiny new DSL up and running thanks to the Eclipse Modeling Technologies and you built a powerful tooling with graphical modelers, textual syntaxes or dedicated editors to support it. But how can you see what is going on when a model is executed ? Don't you need to simulate your design in some way ? Wouldn't you want to see your editors being animated directly within your modeling environment based on execution traces or simulator results?
The GEMOC Research Project designed a methodology to bring animation and execution analysis to DSLs. The companion technologies required to put this in action are small dedicated components (all open-source) at a "proof of concept" maturity level extending proven components : Sirius, Eclipse Debug, Xtend making such features within the reach of Eclipse based tooling. The general intent regarding those OSS technologies is to leverage them within different contexts and contribute them to Eclipse once proven strong enough. The method covers a large spectrum of use cases from DSLs with a straightforward execution semantic to a combination of different DSLs with concurrent execution semantic. Any tool provider can leverage both the technologies and the method to provide an executable DSL and animated graphical modelers to its users enabling simulation and debugging at an early phase of the design.
This talk presents the approach, the technologies and demonstrate it through an example: providing Eclipse Debug integration and diagram animation capabilities for Arduino Designer (EPL) : setting breakpoints, stepping forward or backward in the execution, inspecting the variables states... We will walk you through the steps required to develop such features, the choices to make and the trade-offs involved. Expects live demos with simulated blinking leds and a virtual cat robot !
The increasing demand for computing power in fields such as biology, finance, machine learning is pushing the adoption of reconfigurable hardware in order to keep up with the required performance level at a sustainable power consumption. Within this context, FPGA devices represent an interesting solution as they combine the benefits of power efficiency, performance and flexibility. Nevertheless, the steep learning curve and experience needed to develop efficient FPGA-based systems represents one of the main limiting factor for a broad utilization of such devices.
In this talk, we present CAOS, a framework which helps the application designer in identifying acceleration opportunities and guides through the implementation of the final FPGA-based system. The CAOS platform targets the full stack of the application optimization process, starting from the identification of the kernel functions to accelerate, to the optimization of such kernels and to the generation of the runtime management and the configuration files needed to program the FPGA.
ISC Frankfurt 2015: Good, bad and ugly of accelerators and a complementary pathJohn Holden
Accelerators Vs Adjoint Algorithmic Differentation (AAD).... NONSENSE. It is not a choice. The two can be combined to provide the ultimate accelerator. Accelerators such as NVIDIA GPUs, Intel Xeon Phis CAN be combined with AD. NAG has the software tools and expertise to deliver AD solutions for traditional architectures and accelerarors
We propose a discrete-time formulation for optimization of scheduling in crude-oil refineries considering both the logistics details practiced in industry and the process feed diet and quality calculations. The quantity-logic-quality phenomena (QLQP) involving a non-convex mixed-integer nonlinear (MINLP) problem is decomposed considering first the logistics model containing quantity and logic variables and constraints in a mixed-integer linear (MILP) formulation and, secondly, the quality problem with quantity and quality variables and constraints in a nonlinear programming (NLP) model by fixing the logic results from the logistics problem. Then, stream yields of crude distillation units (CDU), for the feed tank composition found in the quality calculation, are updated iteratively in the following logistics problem until their convergence is achieved. Both local and global MILP results of the logistics model are solved in the NLP programs of the quality and an ad-hoc criteria selects to continue those among a score of the MILP+NLP pairs of solutions. A pre-scheduling reduction to cluster similar quality crude-oils decreases the discrete search space in the possible superstructure of the industrial-sized example that demonstrates our tailor-made decomposition scheme of around 3% gap between the MILP and NLP solutions.
Enterprise-Wide Optimization for Operations of Crude-Oil Refineries: closing ...Brenno Menezes
We propose a quantitative analysis of an enterprise-wide optimization for operations of crude-oil refineries considering the integration of planning and scheduling to close the decision-making gap between the procurement of raw materials or feedstocks and the operations of the production scheduling. From a month to an hour, re-planning and re-scheduling iterations can better predict the processed crude-oil basket, diet or final composition, reducing the production costs and impacts in the process and product demands with respect to the quality of the raw materials. The goal is to interface planning and scheduling decisions within a time-window of a week with the support of re-optimization steps. Then, the selection, delivery, storage and mixture of crude-oil feeds from the tactical procurement planning up to the blend scheduling operations are made more appropriately. The up-to-down sequence of solutions are integrated in a feedback iteration to both reduce time-grids and as a key performance indicator.
FEEDSTOCK STORAGE ASSIGNMENT IN PROCESS INDUSTRY QUALITY PROBLEMS (Poster)Brenno Menezes
We propose a mixed-integer linear (MILP) model for the design of assignments of various raw materials with different qualities when moving them from external supply sources to shared storages. This is especially important in process industries with limited storage and quality blend programs optimizing a plant feed diet for ongoing operations involving process units, inventory control and product demands, as found in crude-oil, ore/metal and food processing industries. This novel storage assignment problem minimizes the quality deviation when a larger number of feedstocks from marine vessels or ships are clustered into a smaller number of containers or storages in the plant, known as the Pigeonhole Principle, allocating the raw material to a definite place in an orderly system. Although the model only uses raw material quality data and neglects logistics details such as raw material supply amounts, timing and volume available in the storage, the simplification can be partially circumvented by splitting the raw material into two or more species with same qualities in order to fit into the storages. Examples dealing with 5 to 45 different crude-oil feedstocks clustered into 4 storage tanks demonstrate the proposed model, which yields the optimum storage assignment within minutes for industrial-scale problems.
FEEDSTOCK STORAGE ASSIGNMENT IN PROCESS INDUSTRY QUALITY PROBLEMSBrenno Menezes
We propose a mixed-integer linear (MILP) model for the design of assignments of various raw materials with different qualities when moving them from external supply sources to shared storages. This is especially important in process industries with limited storage and quality blend programs optimizing a plant feed diet for ongoing operations involving process units, inventory control and product demands, as found in crude-oil, ore/metal and food processing industries. This novel storage assignment problem minimizes the quality deviation when a larger number of feedstocks from marine vessels or ships are clustered into a smaller number of containers or storages in the plant, known as the Pigeonhole Principle, allocating the raw material to a definite place in an orderly system. Although the model only uses raw material quality data and neglects logistics details such as raw material supply amounts, timing and volume available in the storage, the simplification can be partially circumvented by splitting the raw material into two or more species with same qualities in order to fit into the storages. Examples dealing with 5 to 45 different crude-oil feedstocks clustered into 4 storage tanks demonstrate the proposed model, which yields the optimum storage assignment within minutes for industrial-scale problems.
CRUDE-OIL BLEND SCHEDULING OPTIMIZATION OF AN INDUSTRIAL-SIZED REFINERY: A DI...Brenno Menezes
We propose a discrete-time formulation for optimization of scheduling in crude-oil refineries considering both the logistics details practiced in industry and the process feed diet and quality calculations. The quantity-logic-quality phenomena (QLQP) involving a non-convex mixed-integer nonlinear (MINLP) problem is decomposed considering first the logistics model containing quantity and logic variables and constraints in a mixed-integer linear (MILP) formulation and, secondly, the quality problem with quantity and quality variables and constraints in a nonlinear programming (NLP) model by fixing the logic results from the logistics problem. Then, stream yields of crude distillation units (CDU), for the feed tank composition found in the quality calculation, are updated iteratively in the following logistics problem until their convergence is achieved. Both local and global MILP results of the logistics model are solved in the NLP programs of the quality and an ad-hoc criteria selects to continue those among a score of the MILP+NLP pairs of solutions. A pre-scheduling reduction to cluster similar quality crude-oils decreases the discrete search space in the possible superstructure of the industrial-sized example that demonstrates our tailor-made decomposition scheme of around 3% gap between the MILP and NLP solutions.
Industrial View of Crude-oil Scheduling ProblemsBrenno Menezes
We propose replace the full space MINLP by MILP + NLP decomposition for large problems and the partition of crude scheduling in crude assignment and crude blend scheduling.
As continuous-time model cannot be easily implemented by plant operators, the objective is to explore to the limit discrete-time models:
- 8h-step (shift) for 2-4 weeks (42-84 periods)
- 2h-step for 7 days (84 periods)
- 1h-step for 4 days (48 periods)
Integration Strategies for Multi-scale Optimization in the Oil-refining IndustryBrenno Menezes
We propose a multi-scale optimization involving process design synthesis, supply chain coordination and refinery operations (in planning, scheduling and RTO) considering simultaneous and decomposed strategies for handling the hierarchy between the levels, the relationships among the entities and the nonlinearity inherent to the oil-refining industry.
Smart Process Operations in Fuels Industries: Applications and Opportunities ...Brenno Menezes
The smart revolution, the 4th after the mechanical, electrical and digital ones, increasingly becomes a reality inside the oil and gas industries. Based on six smart fundamentals: 1-) easy to implement, 2-) integrates parts not yet integrated, 3-) uses actual plant data, 4-) reduces the optimization search space, 5-) tries to boost the polyhedral space of optimization and finally 6-) automated-execution for faster and better solutions. We start by identifying the challenges in advanced planning and scheduling inside petroleum refining industries. Specifically, three "smart" process operations around scheduling optimization are explained. The 1st is what we call the crude-oil to tank assignment problem (CTAP) where a mixed-integer linear model determines the design or destination of crude-oils (feedstocks) transferring from terminals to refinery storage tanks in order to minimize the a) deviation of quality of each crude-oil, b) reduce the optimization search space in further crude-oil scheduling optimization and c) boost the polyhedral space of optimization. This is to prepare for the transfers of crude-oil charged to the tanks by considering the most important (key) quality bottleneck constraints that drive the economics, efficiency and emissions of the refinery. The 2nd smart application is a nonlinear optimization integrating the distillation blending and cutpoint temperature optimization using experimental plant data (ASTM or SD) of the atmospheric tower distillation curves to define new initial and final boiling points (cutpoints) of the distillates by considering the market demands of the blend-shops for the final fuel products. The 3rd is a "data-driven" real-time optimization (RTO) implemented using an LP to integrate the multiple continuous-process units in the refinery as a whole using well-known closed-loop parameter estimation techniques in IMPL to estimate steady-state gains or first-order derivatives from routine plant operating data. IMPL is the modeling and solving platform to be introduced and discussed further given that it provides an integrated environment to develop and deploy industrial applications of this nature. In addition, it should be emphasized that these types of applications can be applied to other process industries especially in the oil and gas and related industries.
Scheduling Optimization for Regeneration of Ion Exchange Resin in a Demineral...Brenno Menezes
A mixed-integer linear programming (MILP) model is developed to determine optimal decisions concerning resource selection and sequencing operations over time in a water treatment facility (Fig. 1). Processing and cleaning timetables for a network of ion exchange resin beds are determined obeying specialized operational business rules. Measurement of electrical conductivity in the water treatment outlet alerts the necessity of regeneration as safeguard to avoid ion contamination in the treated water tank (buffer tank), updating the volume or time to saturation for the next optimization run.
Smart Process Operations in Fuels Industries: Applications and Opportunities ...Brenno Menezes
The smart industrial revolution, the 4th after the mechanical, electrical, and digital ones, is a today’s process industry frontline research in terms of technology, but also in terms of the use of technology that implies in human behavior and resource issues to deploy new process-of-work. Smart operation makes use of new information and communication technologies (ICT) and advanced algorithms such as optimization[1], therefore there are requirements for high qualified and trained teams to handle such technologies[2].
Smart Process Manufacturing (SPM) also known as Industry 4.0 is an emerging field of research and refers to a design and operational paradigm involving the integration of measurement and actuation, safety and environmental protection, regulatory control, high fidelity modeling, real-time optimization and monitoring, and planning and scheduling[3]. It is the enterprise-wide application of advanced technologies, tools, and systems, coupled with knowledge-enabled personnel, to plan, design, build, operate, maintain, and manage process manufacturing facilities, where is expected reduced costs in inventories, manufacturing, logistics, maintenance, etc[4].
We present several applications and opportunities of smart operations to be explored in fuels industries. These operations improve the decision-making by (i) clustering crude oil arriving in the refineries as much as different they can be in terms of quality[5]; (ii) integrating cutpoint temperature optimization of the distillates (initial and final boiling points) with the final blending producing specified fuels[6] and (iii) hybrid real-time optimization considering steady-state gain in an LP modeling.
(1) Thornhill NF, 2015. In https://workspace.imperial.ac.uk/smartops/Public/ENERGY-SMARTOPSOverview.pdf
(2) Christofides PD, David JF, El-Farra NH, Clark D, Harris KRD and Gipson JN. Smart plant operations: vision, progress and challenges. Aiche Journal, 2007, 53 (11), 2734–2741.
(3) Davis JF and Edgar TF. Smart Process Manufacturing – A Vision of the Future. In Book: Design for Energy and Environment. Ed. El-Halwagi MM, Linninger AA, 2008, 150-165.
(4) David JF, Edgar T, Porter J, Bernaden J and Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng, 2012, 47, 145–156.
(5) Industrial Algorithms LLC., 2015. In http://pt.slideshare.net/alkis1256/ctap-imf.
(6) Kelly JD, Menezes BC, Grossmann IE. Distillation Blending and Cutpoint Temperature Optimization using Monotonic Interpolation. Ind Eng Chem Res, 2014, 52, 18324-18333.
Quantitative Methods for Strategic Investment Planning in the Oil-Refining In...Brenno Menezes
Unlike traditional process design scenario-based methodologies to construct complex oil-refinery process network, discrete optimization approaches in MINLP and in an iterative MILP+NLP solve the capital investment planning problem to predict unit capacity increments (expansion or installation) for an integrated multi-site refinery problem considering resources such as capital and raw/intermediate material, processing and blending capabilities, market demands and project constraints.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Crude-Oil Scheduling Technology: moving from simulation to optimization
1. Crude-Oil Scheduling Technology: moving from
simulation to optimization
1Refining Optimization, PETROBRAS Headquarters, Rio de Janeiro, Brazil.
2Center for Information, Automation and Mobility, Technological Research Institute, São Paulo, Brazil.
Brenno C. Menezes,1,2 Marcel Joly,1,2 Lincoln F. L. Moro1
Upstream Downstream Distribution Gas & Energy Biofuels
ESCAPE25, Copenhagen, Jun 2nd, 2015
4. 1- Scheduling Technology in PETROBRAS (home-grown solution SIPP)
2- Workshop on Commercial Scheduling Technologies in Oct, 2013
3- Refactoring/Remaking of SIPP: GUI + IT Developments
Modeling + Engineering Advancements
4- Applications of Optimization (CTA+ISW, DBCTO, MOVPath, Demi-Water)
5- Opportunities (CTA+ISW+DBCTO, Bottleneck Scheduling, Smart Operations)
6- Conclusions
Outline
5. 5
Scheduling Technology in PETROBRAS
Space
Time
Supply
Chain
Refinery
Process
Unit
second hour day month year
RTOControl
on-line off-line
Scheduling
Operational
Planning
Tactical
Planning
Strategic
Planning
Simulation
Petrobras
NLP Optimization
Commercial (Aspentech)
LP Optimization
Petrobras
Operational Corporate
SIPP: Integrated System for Production Scheduling
week
6. 6
What to do?
How and When to do?
Crude transf./receiving/diet
Process unit operations
Blending
Inventories
Deliveries
SheWhart or PlanDoCheckAct (PDCA) Management Cycle
Scheduling Technology in PETROBRAS
(Joly et al., 2015)
estimation
7. 7
Operational Planning (MINLP): (Neiro and Pinto, 2005)
Strategic Planning (MILP and MILP+NLP): (Menezes et al., 2015ab)
(Menezes , Kelly & Grossmann, 2015a): Phenomenological Decomposition Heuristic , ESCAPE25
(Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE
Goal: Multi-Site Scheduling
8. SIPP and Other Initiatives for Scheduling
SIPP
ARAUCARIASMART
Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Fuels
Blending
Inventories
Crude Oil
Blending
SMART:
- Genetic Alg. model
using non-optimized
starting points
ARAUCARIA
- Continuous-time
impossible to be
executed in practice
Crude Oil
Receiving
Initiative Pitfalls:
9. Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Product
Blending
Crude Oil
Receiving Inventories
Inventory control
Yields updated by hand
Crude heavy/light and sour/sweet
Blending indices from literature
Scheduling is
Worst Case Best Case
Crude, Units, Inventories, Deliveries
Yields updated automatically
Crude in several properties/yields
Blending using daily data/interp.
Crude Oil
Blending
11. 11
As a normal outcome, schedulers abandon these solutions and then
return to their simpler spreadsheet simulators due to:
(i) efforts to model and manage the numerous scheduling scenarios
(ii) requirements of updating premises and situations that are
constantly changing
(iii) manual scheduling is very time-consuming work.
SIPP’s or Simulation-based Solution Problems
“Automation
-of-Things”
(AoT) Automated Data Integration = IT Development
Automated Decision-Making = Optimization
Automated Data Integrity = Data Rec./Par. Est.
Needs of
12. 12
Simulation X Optimization
Simulation
Pros
• Wide-refinery simulation
• Familiar to Scheduler
• Quick solution (can be
rigorous)
Cons
• Trial-and-error
• Only feasible solution
Optimization
Pros
• Automated search for a
feasible solution
• Optimized solution (Local)
Cons
• Optimization of subsystems
• Solution time can explode
• High-skilled schedulers
• Global optimal (dream)
13. Workshop on Commercial Scheduling
Technologies in Oct, 2013
(Joly et al., 2015) M3Tech
Honeywell
SIMTO
Production Scheduler
Out of the market
14. GAMS
Pre-Formatted (Simulation) Modeling Platform (Optimization)
Soteica
IMPL
AIMMS
Off-Line
On-Line
Average
Price
10k (dev.) and 20k (dep.) +20% year100 k/year
(per tool)
Modeling Built-in
facilities
Without
facilities
Black
Box
Demanded Tools 1 13
Configuration Coding Configuration
Workshop on Commercial Scheduling
Technologies in Oct, 2013
OPL
15. - Drawer to generate flowsheet structures (Visual Prog. Lang.)
- Upper and lower bounds for yields (more realistic)
- Pre-Solver to reduce problem size and debug "common" infeas.
- Proprietary SLP to solve large-scale NLPs (called SLPQPE)
- Names-to-numbers to generate large models very quickly
- Ability to add ad-hoc formula (e.g., blending rules)
- Generates analytical quality derivatives using complex numbers
- Initial value randomization to search for better solutions
- Digitization/discretization engine (continuous-time data input)
IMPL Important Techniques/Features
(Industrial Modeling and Programming Language)
16. Modeling and Programming Languages Aspects
- Same process unit models for planning and scheduling
- Planning & scheduling with data-mining, MPC, data rec., RTO
- CDU(N) and VDU(M) as hypos, pseudo-components or micro-
cuts for any NxM arrangement (towers in cascade)
- Hierarchical Decomposition Heuristics HDH (Kelly & Zyngier, 2008)
- Phenomenological Decomposition Heuristics PDH: the MINLP
model is partitioned in MILP and NLP (Menezes, Kelly & Grossmann,
ESCAPE25, 2015)
17. 1- APS (Advanced Planning and Scheduling):
Planning: Aspen, Soteica
Scheduling: Aspen, Princeps, Soteica, Invensys
Blending: Aspen, Princeps, Invensys
2- APC (Advanced Process Control): Aspen, gProms
3- RTO (Real-Time Optimization): Aspen, Invensys
4- Data Reconciliation and Parameter Estimation: Aspen, KBC, Soteica
5- Hybrid Dynamic Simulation: Aspen, KBC, Invensys
6- Differential Equation Solution (ODE and PDE): gProms
Applications in IMPL
18. 1st STEP: separate (GUI + IT) from (Modeling + Engineering)
2nd STEP: prototype (ModEng) using easy-to-use modeling language
3rd STEP: prototype (GUI+IT) in a reactive iteration with 2nd STEP
30% 30%30%
GUI
(Graphic User Interface)
Interfacing/database Modeling+Engineering
10%
Solver
GUI + IT Modeling + Engineering
Refactoring/Remaking of SIPP
4th STEP: integrate (GUI + IT) and (Modeling + Engineering)
19. GUI + IT Developments
30%30%
GUI
(Graphic User Interface)
Interfacing/database
GUI + IT
Plant
(Visio)
Database
(Oracle)
Simulation
(Visual C++)
IHM
(Delphi)
Movement and Mixing
Optimization Management
GOMM
New GUI in C#
20. Modeling + Engineering Advancements
30%
Modeling+Engineering
10%
Solver
Modeling + Engineering
1st: Refinery Teams should be
involved in the modeling
Demand: easy-to-use tools
2nd: Optimize subsystems and
integrate them incrementally
HQ R&D
Center
Refineries
Universities
IT Develp.
Center
Petrobras case:
- HQ + CMU + São Paulo/Rio
Universities
- R&D
Center
Several Brazilian
Universities
+
Research Phase Development Phase
(5-10 years) (1-3 years)
dataflow or diagrammatic programming
21. IMPL’s UOPSS Visual Programming Language using DIA
Variable Names:
v2r_xmfm,t: unit-operation m flow variable
v3r_xjifj,i,t: unit-operation-port-state-unit-operation-port-state ji flow variable
v2r_ymsum,t: unit-operation m setup variable
v3r_yjisuj,i,t: unit-operation-port-state-unit-operation-port-state ji setup variable
VPLs (known as dataflow or diagrammatic programming) are based on the idea of "boxes and
arrows", where boxes or other screen objects are treated as entities, connected by arrows,
lines or arcs which represent relations (node-port constructs). (Bragg et al., 2004)
x = continuous variables (flow f)
y = binary variables (setup su)
j
22. 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (1)
𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ 𝐦, 𝐭 (2)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(3)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(4)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭
(5)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇 𝒎 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ∀ (𝐦, 𝐣), 𝐭
(6)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭
(7)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒇𝐣,𝒊 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕 ∀ (𝐣, 𝐢), 𝐭 (8)
j
Semi-continuous
equations for units
Semi-continuous
equations for streams
Mixer for each i, but
using lo/up bounds
Splitter for each j, but
using lo/up bounds
23. 𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(9)
𝐣∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚𝐢,𝒎 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐢, 𝐦), 𝐭
(10)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≥ 𝑳𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭
(11)
𝐢∈(𝐣,𝐢)
𝐯𝟑𝐫_𝒙𝒋𝒊𝒇𝐣,𝐢,𝒕 ≤ 𝑼𝑩𝒚 𝐦,𝒋 𝐯𝟐𝐫_𝒙𝒎𝒇 𝐦,𝒕 ∀ (𝐣, 𝐦), 𝐭
(12)
𝐦(𝐦∈𝐮)
𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≤ 𝟏 ∀ 𝐮, 𝐭
(13)
𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝒎′,𝒕 + 𝐯𝟐𝐫_𝒚𝒎𝒔𝒖 𝐦,𝒕 ≥ 𝟐 𝐯𝟑𝐫_𝒚𝒋𝒊𝒔𝒖𝐣,𝐢,𝒕∀ 𝒎′
, 𝒋 , (𝐢, 𝐦) (14)
(Menezes , Kelly, Grossmann & Vazacopoulos, 2015b): Generalized Capital Investment Planning, CACE
xX
xX
x
x
j
Several unit feeds
(treated as yields
with lower and
upper bounds)
Selection of modes
in one physical unit
Structural
Transitions
26. Crude Tank Assignment + Improved Swing Cut
(CTA) (ISW)
Kerosene
Light Diesel
ATR
CDU
C1C2
C3C4
SW1
SW2
SW3
VR
VDU
N
K
LD
HD
D1HT
Naphtha
Heavy Diesel
LVGO
HVGO HTD2
D2HT
HTD1
to hydrotreating
and/or reforming
(To FCC)
Crude C
Crude D
(To Delayed Coker)
to hydrotreating
to caustic and
amines treating
JET
GLN
FG
LPG
VGO
FO
Final Products
MSD
HSD
LSD
Crude A
Crude B
(Menezes, Kelly & Grossmann, 2013)(IAL, 2015)
Clusters or Crude Tanks
Crude
Min cr,pr(Crude-Cluster)2
cr crude
pr property
pr ou yields: naphtha-yield (NY), diesel-yield (DY), diesel-sulfur (DS) and residue-yield (RY)
Improve the flexibility in the search for
optimized diet/recipe/blend
27. Distillation Blending and Cutpoint Temperature
Optimization (DBCTO) (Kelly, Menezes & Grossmann, 2014)
From Other
Units
From CDU
Kerosene
Light Diesel
ATR
C1C2
C3C4
N
K
LD
HD
Naphtha
Heavy Diesel
Crude
CDU
ASTM D86
TBP
Inter-conversion
Evaporation
Curves
Interpolation
Ideal Blending
Evaporation
Curve
Multiple
Components
Final
Product
ASTM D86
Interpolation
Inter-conversion
TBP
𝐘𝐍𝐓𝟗𝟗 = 𝟎. 𝟗𝟎 +
𝟎. 𝟗𝟗 − 𝟎. 𝟗𝟎
𝐎𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎
𝐍𝐓𝟗𝟗 − 𝐎𝐓𝟗𝟎
𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟏𝟎 −
𝟎. 𝟏𝟎 − 𝟎. 𝟎𝟏
𝐎𝐓𝟏𝟎 − 𝐎𝐓𝟎𝟏
𝐎𝐓𝟏𝟎 − 𝐍𝐓𝟎𝟏
𝐃𝐘𝐍𝐓𝟎𝟏 = 𝟎. 𝟎𝟏 − 𝐘𝐍𝐓𝟎𝟏
𝐃𝐘𝐍𝐓𝟗𝟗 = 𝐘𝐍𝐓𝟗𝟗 − 𝟎. 𝟗𝟗
𝐎𝐥𝐝 𝐓𝐞𝐦𝐩𝐞𝐫𝐚𝐭𝐮𝐫𝐞: 𝐎𝐓
New Temperature: NT
New Yield: YNT
Difference in Yield: DYNT
28. Crude Oil
Transferring
Refinery Units Fuels
Deliveries
Product
Blending
Crude Oil
Receiving
Inventories
Opportunities in CTA+ISW+DBCTO
CTA
ISW DBCTO
New-SIPP with optimization
GOMMCrude Oil
Blending
New-SIPPOT
inside GOMM
to register the
execution of
the scheduling
29. Bottleneck Scheduling
Step 1: Identify Key Bottlenecks (see below)
Step 2: Design Optimization Strategy
Step 3: Determine Information Requirements
Step 4: Prototype and Implement, etc.
Quantity-related:
Inventory containment
Hydraulically constrained
Logic-related (Physics):
Mixing, certification delays, run-lengths, etc.
Sequencing and timing
Quality-related (Chemistry):
Octane limits on gasoline
Freeze and cloud-points on
kerosene and diesels, etc
Step 5: Capture Benefits Immediately
(Harjunkoski, 2015)
Scheduling Solution Development Curves
30. Smart Operations
(Qin, 2014)(Christofides et al., 2007)
(Davis et al., 2012)
(Huang et al., 2012)
(Chongwatpol and Sharda, 2013)
(Ivanov et al., 2013)
Smart Process Manufacturing Big Data RFID in APS and Supply Chain
Opportunity for Molecular Scheduling for a selected crude feed
Example: when crude is selected for 2-4 days, after the 1st shift of 8h update all
data using Information and Communication Technologies (ICT) integrated with
Data-Mining applications and then use this in the Decision-Making
31. 31
• Partnership Industry-Academia is fundamental for modeling advances.
Our vision it is missing some RPSE section, initiative, journal, meeting, etc.
• Automated DMs (Decision-Making and Data Mining)
• Permit schedulers to model using VPL in diagrammatic programming
• When moving from simulation to optimization:
Conclusions
- Optimize subsystems and then, if necessary, integrate them
incrementally
- Integrate distillates cutpoints and blending using daily data in
today’s operations as well as hydrotreating severity, etc.
- Be sure the data is accurate otherwise the decision is bad despite
the modeling
