Data-driven Optimization: Applications to Energy Infrastructure and Process Industry

Nowadays, the existence and ease of access to massive amounts of data encourage proposing data-driven solutions. As optimization has always been based on the interchange between models and data, high-level optimization tasks such as planning and scheduling will extremely benefit from information mined from massive data sets. The development of big data tools (i.e., machine learning) has proven superiority over traditional data tools in dealing with vast amounts of data, data with undefined structure and capturing important information from data in a very efficient and computationally tractable manner. Therefore, in this work, big data tools are implemented to address the challenges associated with planning models of energy infrastructure that incorporate renewable resources and chemical engineering processes, namely, uncertainty handling, multiscale modelling, and unit process equation complexity. A Data-driven stochastic optimization framework that leverages big data in design and operation of power generation planning is proposed. A k-means clustering algorithm is adopted to generate uncertainty scenarios for the stochastic optimization framework. These scenarios are used as inputs to the stochastic model where the proposed model is formulated as a mixed integer linear program (MILP) and solved using GAMS. The proposed approach is applied to different power planning models that include unit commitment (UC) characteristics where the size of uncertainty scenarios is reduced. Results show that the proposed approach is an effective tool to generate reduced size stochastic scenarios. The design and operation of energy hub problem involves the integration of decision levels with different time scales that usually lead to multiscale models which are computationally expensive. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to a high level of intermittency associated with renewable energy. A mathematical programming-based general clustering approach is applied to reduce the size of multiple attributes demand data and tackle the computational complexity of multiscale energy hub problems. Multiscale with multiple attributes energy hub incorporating hydrogen storage is modelled as a MILP stochastic optimization problem under wind uncertainty. Different case studies are generated under different environmental consideration to assess the efficiency of the clustering approach and stochastic formulation. Assessments conclude that the clustering approach is an effective tool to reduce the size of the original model while maintaining good results. Recent advancements in supervised machine learning tools have demonstrated their ability to achieve accurate and efficient prediction results. Therefore, in this study, these tools are employed as alternative approaches to model a specific application in the gas industry. The chosen application is a natural gas condensate stabilization process based on operating data. Natural gas condensate treatment involves condensate stabilization process in which light end components are removed and thus condensate vapour pressure is reduced to meet storage and transportation specification. Different supervised machine learning models are developed to predict the performance of two industrial condensate stabilizer units. Large datasets of the two different industrial condensate stabilizers, including operating data of input-output variables, are utilized to develop and evaluate these models. The main purpose of developing these machine learning models is to predict the important parameters of the final stabilized liquid. Results attained from this study showcase the capability of the developed models to offer reliable and accurate predictions. A data-driven surrogate-based optimization framework is developed, where the generated machine learning models can serve as a convenient replacement for detailed first principle models, to find the optimal values of the variables corresponding to the minimal operational energy consumption. The proposed framework can benefit the gas industry to simultaneously achieve process efficiency, profitability, and safety

Tapping Singular Middle Eastern Ultrasour Gas Resources Combining Membrane and Absorption Systems: Potential for Energy Intensity Reduction

A process design and techno-economic analysis is proposed for sweetening ultrasour natural gas containing over 20% H₂S and 30% total acid gases using a hybrid scheme approach. This type of gas resource is unique and can only be found in the Middle East. The hybrid scheme combines membrane and amine gas absorption systems. The study was made on the basis of process simulations and sensitivity analyses to find the most suitable process design and operating parameters using the software ProMax. A Pebax-based membrane module(s) is used as the primary sweetening method, whereas gas absorption is applied to meet the final gas product specifications. The hybrid scheme is benchmarked against the current stand-alone absorption system used to process this rare type of gas. It was found that the gas sweetening energy intensity can be substantially reduced using the hybrid scheme and be more cost-effective than conventional stand-alone absorption units for treating Middle Eastern ultrasour natural gas

An integrated electric vehicle network planning with economic and ecological assessment: Application to the incipient middle Eastern market in transition towards sustainability

The acceptance of electric vehicles (EVs) is attaining momentum as a cleaner alternative to internal combustion vehicles. Two of the United Arab Emirates’ (UAE) key priorities are infrastructure expansion and environmental sustainability. Hence, the government has proposed the incorporation of EVs into the transportation network to minimize fossil fuel depletion and energy subsidies. This study proposes a power supply chain network model for EVs accounting for upstream and downstream components. The goal is meeting a region’s power demand following environmental and operating restrictions. The power supply chain is represented using an integer linear program (MILP) model in a multi-period fashion. The UAE’s capital was taken as case study for the time period 2020 to 2030. The outcomes suggest natural gas electricity still dominates, nonetheless at a smaller degree, while near 660 charging points are required to meet the demand of almost 16,000 EVs by 2030

Minimal underlying data.

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CONSORT 2010 checklist of information to include when reporting a randomised trial*.

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List of interventions.

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S2 File -

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The Saudi Critical Care Trials Group.

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Change of daily process measures over the study period among the whole cohort and in subgroups of ICUs with baseline spontaneous trial (SAT) compliance of >50% and ≤50.

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