Recent advances in gas-to-liquids process intensification with emphasis on reactive distillation

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Process intensification (PI) is a branch of process synthesis that encompasses and impacts a number of process technologies. Research in PI has recently gained considerable attention due to challenges related to energy and the environment, alongside risks in capital investment decisions. These challenges necessitate the development of optimization-based computational tools for process synthesis and design, which enable the integration of multiple phenomena that occur at different scales in an intensified unit. Current efforts in the field are yielding promising results that indicate the transformation of the petrochemical industry. This review highlights the role that PI can play in the production of fuels, chemicals and electricity from natural/shale gas, using reactive distillation with a Fischer–Tropsch-based gas-to-liquids application. We also discuss several related gas processing PI applications along with recent algorithmic developments for process synthesis.

Introduction

Over the past two decades, Process Intensification (PI) has received significant attention, due to its potential to realize innovative and more sustainable processes. In practice, PI has been characterized by novel equipment designs that produce dramatic process improvements including reduction in size, waste production, energy consumption and process safety [1, 2, 3]. A recent monograph on process intensification [4] provides detailed presentations of the elements of PI, ranging from the integration of heat and mass transfer, separations, reactive systems to dynamics and control. Noteworthy advances have been reported for reactive distillation (RD), compact heat exchangers, microwave heat and mass transfer as well as integrated membrane separation and reaction. As a result, PI is an essential enabling strategy for new opportunities that greatly benefit the process industries.

The availability of shale gas in the United States has opened up opportunities for gas-to-liquids (GTL) processes. Despite market uncertainties, there are still plans to establish small-scale and large-scale GTL facilities [5]. Moreover, with the current low price of natural gas as well as pressing environmental and social concerns on shale gas industry, it is imperative to design and optimize shale gas energy systems that are economically efficient, environmentally sustainable, and socially responsible [6]. Toward this goal, the main research challenge is to develop an integrated energy systems modeling framework that can systematically generate optimal designs and operational strategies, and comprehensively improve multiple sustainability criteria [6]. Moreover, multiphase reaction engineering and PI can play a critical role in developing technologies to unlock the value and opportunities of shale gas [7], and to mitigate carbon footprints in providing sustainable options for energy and chemicals production.

Toward this end, advanced model-based optimization strategies provide potential step changes in energy efficiency and capital cost reductions, which are required for transforming the chemical process industry. To transform chemical plants to compact, safe, energy-efficient, and sustainable processes, Segovia–Hernández and Bonilla–Petriciolet [4] document a number of advantages to intensified processes, including major savings in capital and operating costs, shorter time to market and less waste.

In this review, we start by giving a high-level description of GTL processes and then follow with recent applications of PI in GTL processes. We then narrow down the focus to Fischer–Tropsch (FT)-based GTL processes emphasizing mostly PI efforts with reactive distillation (RD). Thereafter, we highlight algorithmic developments and then use a small case study to illustrate recent approaches with RD optimization models.

Section snippets

Gas-to-liquids processes

In the exploration and production of shale gas, there is an essential need for optimization-based decision tools that lower carbon emissions and water usage [8]. Drouven and Grossmann [9] used computational models to address long-term, quality-sensitive shale gas development by involving planning, design, and strategic decisions. Gabriel et al. [10] present a case study to evaluate several scenarios for developing water and energy strategies for a GTL process in a region with various demands,

Process intensification in gas-to-liquids systems

Moreover, efficient, modular, and safe technology would enable utilization of smaller shale/natural gas fields, bio-syngas and even off-shore associated gas that otherwise would be flared or re-injected [14]. However, the design and intensification of modular energy systems are a challenging task, as these processes are represented by complex, nonlinear models. Carrasco and Lima [15] proposed an operability-based framework for process design and intensification of modular systems.

A key

Fischer–Tropsch-based gas-to-liquids facilities

Fischer–Tropsch synthesis leads to versatile processes that accommodate various feedstocks, including coal, biomass and natural gas, to produce a range of hydrocarbons that further lead to fuels and/or value-added chemicals [17,18]. Using syngas reactants, FT polymerization allows hydrocarbon chains, attached at one end of the FT catalyst, to grow by sequential addition of single carbon segments single bondCH2single bond, following the reaction:CO + 2H2 → H2O + single bondCH2single bond

Chain growth then terminates by hydrogenation to

Process intensification and reactive distillation

Numerous advances in PI include modularization of reaction systems, membrane reactors, and process flowsheets considerations. Tian et al. [24] recently offered an extensive survey of the state of the art process systems engineering approaches for PI. They highlighted one of the key open questions: how to systematically derive intensified designs [24]. PI encompasses technologies including advanced separation technologies such as divided wall columns [25]; combined reaction–separation

Algorithmic developments in modeling and optimization of RD for FT

Identification of novel intensified designs with optimized process equipment and operating conditions is a challenging task. A systematic strategy to achieve this task is through the formulation and solution of mixed-integer nonlinear programming (MINLP) problems that identify optimal values for the degrees of freedom. These optimization methods are challenged by the large number of discrete variables and by the strong nonconvexity inherent in the RD optimization model, which necessitates

RD modeling case study

The EO-based optimization model for the RD process is based on novel ‘building block’ tray bypass model [41]. For RD, the bypass model is extended to the reactive flash model shown in Figure 2b. Without bypasses, this unit is comparable to an FT slurry reactor. Additional utilities are introduced, and a framework of setting up an adiabatic RD for FT process is presented in Ref. [40••] with optimization formulations to handle internal flows, catalyst loadings, utilities, and a number of trays. A

Conclusion and future perspectives

The decision to build a gas-to-liquid plant has to be based on the perceived future prices of oil and gas; and the prices of specialty chemicals produced from downstream processes. This study shows that an FT-based RD process leads to a low-cost technology that can mitigate this investment risk. Here, Process Intensification is applied so that syngas conversion in multiple reaction and product separation zones can be combined into one unit to realize modular plants with lower capital and

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We gratefully acknowledge the partial support for this work from the National Research Foundation of South Africa (Grant Number: 113652). The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors. The funding bodies accept no liability whatsoever in this regard.

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