Optimal source–sink matching in carbon capture and storage systems with time, injection rate, and capacity constraints

Abstract

Carbon capture and storage (CCS) involves capturing relatively pure carbon dioxide (CO2) from gaseous combustion products and storing it in various reservoirs. In this work, a multiperiod mixed integer linear programming model focusing primarily on physical and temporal considerations of CO2 source–sink matching is proposed. CO2 sources are assumed to be characterized by variable flow rates and fixed operating lives; on the other hand, CO2 sinks are characterized by finite injection rate and storage capacity limits, as well as earliest time of availability. The proposed approach takes into account important temporal issues that may be encountered in planning the CCS system, particularly when the operating lives of sources and sinks do not completely overlap. Two illustrative case studies are then solved to illustrate the use of the model to realistic CCS planning problems. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 411‐416, 2013

A multi-scale framework for CO2 capture, utilization, and sequestration: CCUS and CCU

“…For large-scale CCUS implementation, it is critical to optimally integrate capture, compression, transportation, utilization and sequestration activities. Although CO 2 management using CO 2 capture and sequestration infrastructure has been considered, these studies did not include utilization opportunities (Tan et al, 2013;Kazmierczak et al, 2009;Johnson and Ogden, 2011;Zheng et al, 2010;van den Broek et al, 2009;Brunsvold et al, 2011;Fimbres Weihs et al, 2011;Svensson et al, 2004;Van den Broek et al, 2010;Strachan et al, 2011;Bakken et al, 2008;Middleton et al, 2012;Alhajaj et al, 2013).…”

Scrutinising the Gap between the Expected and Actual Deployment of Carbon Capture and Storage—A Bibliometric Analysis

“…Field F7.3 (50 papers, 23% of C7) encompassed papers that searched for optimal source-sink matching configurations. Key papers included He et al [154] (d = 109), who proposed an MILP model with physical and temporal constraints, in order to handle interval and stochastic uncertainties; Alhajaj et al [155] (d = 109), who presented an integrated whole-system model in order to design an optimum network linking sources and sinks, in so doing describing system behaviour and interactions along a range of length and timescales; Tan et al [156] (d = 97), who developed a continuous-time mixed integer nonlinear programming (MINLP) model that was subsequently converted into an equivalent MILP model; Diamante et al [157] (d = 94), who proposed a graphical approach for optimally matching multiple CO 2 sources and sinks, based on analogies with existing graphical pinch analysis approaches; and Keating et al [158] (d = 85), who based a CCS infrastructure optimisation model on an evaluation of storage uncertainty using a hybrid system model for CO 2 sequestration performance and risk assessment.…”

CO2 Allocation for Scheduling Enhanced Oil Recovery (EOR) Operations with Geological Sequestration Using Discrete-time Optimization

“…Optimization models have been proposed for such problems [1]. Various models have also been developed for CCS sourcesink matching for sequestration purposes [2][3][4]. These models addressed problems with the economics of CCS alone and CCS in combination with EOR, but lack consideration of the optimal scheduling of commencement of EOR for each oil reservoir.…”