A systematic approach for modeling of waterflooding process in the presence of geological uncertainties in oil reservoirs

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2017.12.012 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, a systematic approach which is able to consider different types of geological uncertainty is presented to model the waterflooding process. The proposed scheme, which is based on control and system theories, enables the experts to apply suitable techniques to optimize the production. By using the developed methodology, a reasonable mapping between defined system inputs and outputs in frequency domain and around a specific operating point is established. In addition, a nominal model for the process as well as a lumped representation for uncertainty effects are provided. Based on the proposed modeling mechanism, reservoir management goals can be pursued in the presence of uncertainty by utilization of complicated control and optimization strategies. The developed algorithm has been simulated on 10th SPE-model#2. Observed results have shown that the introduced methodology is able to effectively model the dynamics of waterflooding process, while taking into account the assumed induced geological uncertainty

Importance of microalgae and municipal waste in bioenergy products hierarchy—integration of biorefineries for microalgae and municipal waste processing : A review

In the context of global advancements, the imperative of a sustainable energy supply looms large. Biomass, an adaptable and renewable resource, has garnered attention for its potential contributions, although economic uncertainties persist due to the intricate web of processing pathways. In response, the biorefinery concept emerges as a structured strategy to optimize the processing of microalgae and municipal solid waste (MSW), capitalizing on their multifaceted potential to yield diverse end-products. This review underscores the critical significance of a cohesive biorefinery paradigm that unites the processing of microalgae and MSW, unveiling their capacity to generate a spectrum of high-value products. The utilization of mixed-integer linear programming paves the way for an optimal biorefinery model that navigates through complex decisions. Challenges encompass the array of diverse feedstocks and the preliminary nature of data availability. The overarching goal of this research is to discern optimal pathways for the conversion of MSW and microalgae into energy and valuable products, with a focus on enhancing waste utilization and augmenting the energy supply. In the broader landscape, this comprehensive review advances strategies for sustainable energy generation and waste management, invigorating innovative approaches to shape future progress. By illuminating pathways towards maximizing the potential of biomass resources, this review contributes to the ongoing discourse on sustainable energy and waste utilization

Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces

The final publication is available at Elsevier via https://doi.org/10.1016/j.compchemeng.2019.06.005. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper introduces the development of empirical predictive models and detection methods that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The predictive models investigated in this work are designed based on different techniques such as statistical fingerprinting, artificial neural network (ANN), and multiway projection to latent structures (MPLS). Robustness issues related to each method are discussed and performance comparisons have been done for the presented techniques. Furthermore, model fusion theory has been applied to improve the prediction accuracy of the developed models’ defined output- the value of off-gas water vapor- which is known as one the most vital variables to guarantee a safe and reliable operation. Finally based on the proposed predictive models, a water leak detection methodology is introduced and implemented on an industrial AC EAF and a comprehensive discussion has been done to evaluate the performance of the developed algorithm. To this aim, two fault detection methods have been applied. Fault detection method #1 has been designed using statistical fingerprinting technique, while the other one has been developed based on machine learning-based models and also fusion of the models’ outputs

A Data Size Reduction Approach Applicable in Process Control System of Oil and Gas Plants

In oil and gas plants, the cost of devices applicable for supervising and controlling systems directly depends on the transmission and storage systems, which are related to the data size of process variables. In this paper, process variables frequency-domain and statistical analysis results have been studied to infer if there exists any possibility to reduce data size of the process variables without loss of any necessary information. Although automatic control is not applicable in a shutdown condition, for generalization of the obtained results, unscheduled shutdown data has also been analyzed and studied. The main goal of this paper is to develop an applicable algorithm for oil and gas plants to decrease the data size in controlling and monitoring systems, based on well-known and powerful mathematical techniques. The results show that it is possible to reduce the size of data dramatically (more than 99% for controlling, and more than 55% for monitoring purposes in comparison with existing methods), without loss of vital information and performance quality