Acceleration-sensitive ancillary elements in industrial facilities:alternative seismic design approaches in the new Eurocode

The Eurocode 8—Part 4 approaches, per their December 2022 update, are presented for the design of acceleration-sensitive industrial ancillary components. The seismic performance of such nested and/or supported ancillary elements, namely mechanical and electrical equipment, machinery, vessels, etc. is critical for the safety and operability of an industrial facility in the aftermath of an earthquake. Of primary importance are the structural characteristics of the supporting structure and the supported component, pertaining to resonance, strength, and ductility, and whether these are known (and to what degree) during initial design and/or subsequent modifications and upgrades. Depending on the availability and reliability of information on the overall system, the Eurocode methods comprise (a) a detailed component/structure-specific design accounting for all pertinent component and building characteristics, equivalent to typical building design per Eurocode 8—Part 1–2, (b) a conservative approach where a blanket safety factor is applied when little or no such data is available, and (c) a ductile design founded on the novel concept of inserting a fuse of verified ductility and strength in the load path between the supporting structure and the ancillary element. All three methods are evaluated and compared on the basis of a case-study industrial structure, showing how an engineer can achieve economy without compromising safety under different levels of uncertainty

Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers

Although energy efficiency is quite a cliché term, it is a topic that attracts an increasing attention the last decade, especially in the context of cities and as a means to address emerging challenges like sustainability and climate change. Several models have been introduced to conceptualize and calculate the urban energy system, and to demonstrate the variants that calibrate the local energy efficiency. Nevertheless, cutting-edge technologies like blockchain, electrical -and even autonomous- vehicles, smart building systems, Artificial Intelligence (AI) and big data etc. are growing within cities and question the identified urban energy efficiency, since they demand enormous amounts of power. In this regard, policy makers are concerned of the emerging technologies’ energy efficiency and their impact on the urban energy system and they attempt to introduce corresponding standards for their development. This article focuses on the impact of AI and big data in city's energy efficiency. More specifically, a literature analysis is performed and returned a taxonomy of existing energy efficiency assessment models under the lens of AI and big data. Moreover, the definition of a unified assessment model for AI and big data energy efficiency is approached. © 2021 Elsevier Lt

Integrating uncertainty quantification in reliability, availability, and maintainability (RAM) analysis in the conceptual and preliminary stages of chemical process design

Traditional analysis of a proposed process design uses average input values in the performance assessment model, thereby generating single-point estimates. The resulting estimates ignore reliability, availability, and maintainability (RAM) considerations, or assume a fixed value based on prior experience. As a result, a probabilistic view of the impact of equipment unavailability on process profitability is not considered. Recent works have proposed a financial framework for incorporating safety and sustainability considerations in the analysis of proposed designs. Based on this research, we propose a framework to integrate RAM aspects during the conceptual design stage in a probabilistic manner using Monte Carlo simulation. Subsequently, full distribution profiles of key process performance indicators are generated, including system and section availability, annual net profit, and return on investment (ROI). Probabilistic characterization of equipment availability also facilitates the prediction of potential safety and sustainability issues, as more frequent process upsets may result in increased flaring and other potential negative consequences. A modified availability metric, using restoration instead of repair times, is used in this work to obtain a more accurate view of expected downtime and thus its effects on profitability. A propane dehydrogenation (PDH) process system is used to demonstrate the application and benefits of the framework. The proposed approach allows designers and decision-makers to comprehensively assess the impacts of equipment RAM characteristics on process availability and economic performance. © 2021 Institution of Chemical Engineer

Seismic fragility assessment of high-rise stacks in oil refineries

The seismic fragility is assessed for typical high-rise stacks encountered in oil refineries, namely process towers, chimneys, and flares. Models of varying complexity were developed for the structures of interest, attempting to balance computational complexity and accuracy regarding the structural dynamic and strength properties. The models were utilized along with a set of hazard-consistent ground motions for evaluating the seismic demands through incremental dynamic analysis. Demand/capacity-related uncertainties were explicitly accounted for in the proposed framework. Damage states were defined for each of the examined structure considering characteristic serviceability and ultimate limit states. Τhe proposed resource-efficient roadmap for the analytical seismic fragility assessment of typical high-rise stacks, as well as the findings of the presented research work are available to be exploited in seismic risk assessment studies of oil refineries

Optimal Scheduling of Biodiesel Plants through Property-based Integration with Oil Refineries

This paper addresses the design and scheduling problem of biodiesel plants in conjunction with typical oil refineries via blending of biodiesel and petro-diesel. The feedstocks are often seasonal and their availability and cost usually vary with time. A multi-period scheduling framework is formulated as an optimization problem to determine the optimal feedstock utilization and blending of biodiesel with petro-diesel using a property-integration framework. A case study is solved to illustrate the applicability of the devised approach. 2011 Elsevier B.V.Scopu

A process design approach to manage the uncertainty of industrial flaring during abnormal operations

Flare management challenges are related to the flaring uncertainty during abnormal situations. In this work, a multi-objective optimization framework is upgraded with multi-period optimization and Monte Carlo simulation to incorporate the risk associated with uncertain flare events. An ethylene plant is used to present the developed framework. Using the ethylene process historical flaring data, Monte Carlo simulation generates probabilistic values for flaring events and event duration. Here, cogeneration unit (COGEN) is considered as the flare reduction alternative. The results of the formulations are presented as a set of Pareto fronts providing insights into the competing techno-economic and environmental objectives. Sensitivity analysis on the factors for the case suggests that some factors such as CO2 tax savings are severely affected by minor variations in flaring profiles, whereas others such as the fixed and operating costs are less sensitive. Hence, using this approach, the decision maker gains techno-economic-environmental insights regarding the flare reduction alternative (COGEN).This paper was made possible by NPRP grant No 5-351-2-136 from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the author[s]. The author thanks Ahmed Mhd Nabil AlNouss and Fahd Mohammed for their contribution through managing the historical database and for providing access to the GHG calculator

Application of i-SDT for safer flare management operation

Utilizing unburn flare streams in a safe way represents one of the key challenges during flare alternatives implementation. Most of the time, process safety is considered on a supplemental basis after accomplishing a detailed plant design and economic analysis. The prime reason is the lack of a systematic design tool that facilitates the incorporation of inherently safer design principles into the early stage of process synthesis and in the absence of an adequate amount of data. It would be therefore advantageous for designers if they were able to assess safety aspects in a continuous manner for retrofitting design purposes as well as appraising innovative alternatives. In this work, a newly developed Inherently Safer Design Tool (i-SDT) has been applied to identify reliable and safer operating conditions while implementing a cogeneration (COGEN) unit as a flare utilization alternative. In the illustrative case study, the COGEN unit has been accompanied by an ethylene process to act as an additional utility provider by using some portion of the unburn hydrocarbon streams. These streams were available from several flaring locations of the plant during different routine/abnormal cases. The objective of this work is to conduct a comprehensive techno-economic and environmental performance analysis by utilizing a multi-objective optimization framework along with the necessary set of process constraints derived from the safety perspective offered by i-SDT. The illustrative case study considered here showed that the proposed i-SDT tool could estimate the limits associated with key safety parameters (flammability, toxicity, explosiveness, and reactivity) by explicitly considering operating conditions. Later, these operating limits are explicitly embedded as safety constraints into the optimization algorithm to assess the techno-economic, environmental and safety performance profiles of the process system under consideration. - 2019 The Institution of Chemical EngineersThis paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the author[s].Scopu

Financial Pinch Analysis for Selection of Energy Conservation Projects with Uncertainties

Expenditure for energy utilities is significant for most process plants. The identification and implementation of various energy conservation projects are essential in reducing the operating cost and greenhouse gas emissions associated with energy use. Typically, energy conservation projects need capital investments drawn from limited funding sources. Appropriate selection of these projects is important to ensure overall financial and environmental benefits. Varying energy prices, an evolving carbon emissions regulatory regime, changes in product quality, energy efficiency requirements, and unscheduled maintenance of different process equipment/units make the overall financial returns inherently uncertain. In this work, Financial Pinch Analysis is extended to incorporate uncertainties for the appropriate selection of energy conservation projects. Monte Carlo simulations are performed to account for various sources of uncertainty in financial return metrics for the energy conservation projects. A stochastic linear programming problem is formulated to identify appropriate energy conservation projects. The chance constraint programming method is applied to convert the original stochastic linear programming problem into a deterministic Pinch Analysis framework at different reliability levels. The applicability of the proposed method is illustrated through an example. © 2021, AIDIC Servizi S.r.l

Characterization of Industrial Flaring under Uncertainty for the Design of Optimum Flare Recovery and Utilization Systems

One of the main challenges in industrial applications is to optimally manage flare gases that are inevitably generated both in routine and non-routine process operations but can yet constitute valuable energy resources for process systems. A main challenge is to explore the best possible strategies for exploiting these valuable hydrocarbon streams and propose process design alternatives and operational solutions that achieve maximum recovery and use of flare gases at minimum total cost and considering the uncertainty variations associated with flaring incidents. This requires an understanding of the characteristics of flare streams that affect their recovery and reutilization potential as well as an examination of their impact on process system performance while recognizing that the inherently uncertain nature of flaring calls upon a probabilistic approach. In our study, we examine the impact of using a comprehensive probabilistic analysis framework for process flare streams’ characterization on the design of an optimal recovery and utilization system. In particular, the work aims to explore the impact of uncertainty for key parameters on the design solutions, such as rate of flare occurrences that were assumed constant in other research works (Kazi et al., 2018). Suitable parametrized Monte Carlo (MC) simulations are employed for more accurate flare profile representations. A comparative study is conducted between the base case optimal design and values at risk solutions for cases where flaring variation increases may significantly affect the design features and economic performance of the process system. The proposed framework could inform decision makers’ assessments of the impact of random variations in flaring profiles on process performance profile. © 2022 Elsevier B.V