Strategic and Investment Planning in the Oil-Refining Industry

•

1 gostou•532 visualizações

This document presents quantitative methods for strategic and investment planning in the oil-refining industry. It describes a mixed-integer nonlinear programming model that incorporates blending equations, crude choice and processing constraints, and logic constraints for unit expansions, installations, and investments. The model is used to determine the optimal investments of $30 billion over 12 years to maximize net present value, considering refining structures like crude distillation units and residual fluid catalytic cracking units. The results show it is best to invest in gasoline production using this type of configuration.

Tecnologia

Quantitative Methods for Strategic and Investment Planning in the
Oil-Refining Industry
Brenno C. Menezes1, Ignacio E. Grossmann2, Jeffrey D. Kelly3, Lincoln F. Moro1
1Refining Optimization, PETROBRAS, Rio de Janeiro, Brazil. 2Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, United States. 3IndustrIALgorithms, Toronto, Canada.

Mixed-Integer Problem:

Kerosene

C3 +C3=

Capital Cost Constraint:
Refining Modeling:

Logic Constraints for Expansions and Installations:

Logic Constraints for Unit Type Investments (structural precedence):

Blending Equations:

MINLP Case: 30 bi U$ to invest. Net Present Value (NPV) for 12 years without demand

Crude Choice and Processing Equations:

Conclusion: Trendy into invest in gasoline with CDU and RFCC structure

Mais conteúdo relacionado

Mais de Alkis Vazacopoulos

Automatic Fine-tuning Xpress-MP to Solve MIP

Alkis Vazacopoulos

Data mining 2004

Alkis Vazacopoulos

Amazing results with ODH|CPLEX

Alkis Vazacopoulos

Bia project poster fantasy football

Alkis Vazacopoulos

NFL Game schedule optimization

Alkis Vazacopoulos

2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...

Alkis Vazacopoulos

Posters 2017

Alkis Vazacopoulos

Very largeoptimizationparallel

Alkis Vazacopoulos

Retail Pricing Optimization

Alkis Vazacopoulos

Optimization Direct: Introduction and recent case studies

Alkis Vazacopoulos

Informs 2016 Solving Planning and Scheduling Problems with CPLEX

Alkis Vazacopoulos

EX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment. CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to: Optimize business decisions with high-performance optimization engines. Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios. Create real-world applications that can significantly improve business outcomes. Optimization Direct has partnered with and entered into a technology licensing and distribution agreement with IBM. By combining the founders' industry and software experience and IBM’s CPLEX Optimization Studio product with the arsenal of Optimization modeling and solving tools from IBM provides customers the most powerful capabilities in the industry.

ODHeuristics

Alkis Vazacopoulos

Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.

Missing-Value Handling in Dynamic Model Estimation using IMPL

Alkis Vazacopoulos

Presented in this short document is a description of how to estimate deterministic and stochastic non-parametric finite impulse response (FIR) models in IMPL applied to industrial gas furnace data identical to that found in TSE-GFD-IMF using parametric transfer-functions. The methodology of time-series analysis or system identification involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment/adjustment, auto-, cross- and partial-correlation, etc. to establish the parametric transfer function polynomial degrees especially when we are using non-parametric FIR estimation. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s Sequential Equality-Constrained QP Engine (SECQPE) and Supplemental Observability, Redundancy and Variability Estimator (SORVE). Other types of non-parametric identification known as Subspace Identification (Qin, 2006) and can used to estimate state-space models.

Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...

Alkis Vazacopoulos

Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.

Industrial Modeling Service (IMS-IMPL)

Alkis Vazacopoulos

Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...

Alkis Vazacopoulos

This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992). The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.

Xmr im

Alkis Vazacopoulos

Distillation Curve Optimization Using Monotonic Interpolation

Alkis Vazacopoulos

Presented in this short document is a description of how to model and solve multi-utility scheduling optimization (MUSO) problems in IMPL. Multi-utility systems (co/tri-generation) are typically found in petroleum refineries and petrochemical plants (multi-commodity systems) especially when fuel-gas (i.e., off-gases of methane and ethane) is a co- or by-product of the production from which multi-pressure heating-, motive- and process-steam are generated on-site. Other utilities include hydrogen, electricity, water, cooling media, air, nitrogen, chemicals, etc. where a multi-utility system is shown in Figure 1 with an intermediate or integrated utility (both produced and consumed) such as fuel-gas, steam or electricity. Itemized benefit areas just for better management of an integrated steam network can be found in Pelham (2013) where his sample multi-pressure steam utility flowsheet is found in Figure 2.

Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...

Alkis Vazacopoulos

Presented in this short document is a description of how to model and solve advanced parameter estimation (APE) problems in IMPL. APE is the term given to the application of estimating, fitting or calibrating parameters in models involving a network, topology, superstructure or flowsheet. When estimating parameters with multiple linear regression (MLR), ordinary least squares (OLS), ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) there is no explicit model but simply an X-block and Y-block of data. Hence, these methods are referred to as “non-parametric” or “data-based” methods as opposed to the “parametric” or “model-based” method used here. To solve these types of problems we use what is commonly referred to as “error-in-variables” (EIV) regression which is conveniently implemented as nonlinear data reconciliation and regression (NDRR) using the technology found in Kelly (1998a; 1998b; 1999) and Kelly and Zyngier (2008a). The primary benefit of using EIV (NDRR) over the other regression methods is that we can easily handle the inclusion of conservation laws and constitutive relations, explicitly, a must for any industrial estimation problem (IEP).

Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...

Alkis Vazacopoulos

Mais de Alkis Vazacopoulos (20)

Automatic Fine-tuning Xpress-MP to Solve MIP

Data mining 2004

Amazing results with ODH|CPLEX

Bia project poster fantasy football

NFL Game schedule optimization

2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...

Posters 2017

Very largeoptimizationparallel

Retail Pricing Optimization

Optimization Direct: Introduction and recent case studies

Informs 2016 Solving Planning and Scheduling Problems with CPLEX

ODHeuristics

Missing-Value Handling in Dynamic Model Estimation using IMPL

Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...

Industrial Modeling Service (IMS-IMPL)

Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...

Xmr im

Distillation Curve Optimization Using Monotonic Interpolation

Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...

Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...

Último

Architecting Cloud Native Applications

WSO2

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

DianaGray10

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Orbitshub

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Mcleodganj Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Mcleodganj Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Mcleodganj Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving

Edi Saputra

[BuildWithAI] Introduction to Gemini.pdf

Sandro Moreira

FWD Group - Insurer Innovation Award 2024

The Digital Insurer

Passkeys: Developing APIs to enable passwordless authentication Cody Salas, Sr Developer Advocate | Solutions Architect - Yubico Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...

apidays

Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Zilliz

CNIC Information System with Pakdata Cf In Pakistan

danishmna97

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

DBX First Quarter 2024 Investor Presentation

Dropbox

In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.

Platformless Horizons for Digital Adaptability

WSO2

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

Strategic and Investment Planning in the Oil-Refining Industry

1. Quantitative Methods for Strategic and Investment Planning in the Oil-Refining Industry Brenno C. Menezes1, Ignacio E. Grossmann2, Jeffrey D. Kelly3, Lincoln F. Moro1 1Refining Optimization, PETROBRAS, Rio de Janeiro, Brazil. 2Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, United States. 3IndustrIALgorithms, Toronto, Canada. Mixed-Integer Problem: Kerosene C3 +C3= Capital Cost Constraint: Refining Modeling: Logic Constraints for Expansions and Installations: Logic Constraints for Unit Type Investments (structural precedence): Blending Equations: MINLP Case: 30 bi U$ to invest. Net Present Value (NPV) for 12 years without demand Crude Choice and Processing Equations: Conclusion: Trendy into invest in gasoline with CDU and RFCC structure

Strategic and Investment Planning in the Oil-Refining Industry

Recomendados

Recomendados

Mais conteúdo relacionado

Mais de Alkis Vazacopoulos

Mais de Alkis Vazacopoulos (20)

Último

Último (20)

Strategic and Investment Planning in the Oil-Refining Industry