Optimal Source–Sink Matching in Carbon Capture and Storage Systems with Time, Injection Rate, and Capacity Constraints Raymond R. Tan, a Kathleen B. Aviso, a Santanu Bandyopadhyay, b and Denny K. S. Ng c a Chemical Engineering Department, Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 1004 Manila, Philippines; raymond.tan@dlsu.edu.ph (for correspondence) b Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, Maharashtra, India c Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, University of Nottingham, Malaysia, Selangor 43500, Malaysia Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ep.11630 Carbon capture and storage (CCS) involves capturing rela- tively pure carbon dioxide (CO 2 ) 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 CO 2 source–sink matching is proposed. CO 2 sources are assumed to be characterized by variable flow rates and fixed operating lives; on the other hand, CO 2 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 plan- ning 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. Ó 2012 American Institute of Chemical Engineers Environ Prog, 00: 000–000, 2012 Keywords: Carbon capture and storage, source–sink matching, mixed integer linear programming INTRODUCTION The growing global concern about climate change is one of the main forces driving research on the reduction of car- bon dioxide (CO 2 ) emissions from various energy systems such as power plants and industrial facilities. These mitiga- tion strategies include energy efficiency enhancement, fuel substitution (e.g., biomass co-firing), utilization of low-carbon technologies (e.g., renewable or nuclear energy), and use of carbon capture and storage (CCS). The latter option is con- sidered to be attractive mainly because CCS can enable the continued use of fossil fuels such as coal and natural gas, while drastically reducing CO 2 emissions generated during combustion [1, 2]. In general, CCS involves capturing rela- tively pure CO 2 from gaseous combustion products and stor- ing it in various reservoirs. Carbon capture techniques include relatively mature technologies such as postcombus- tion capture via flue gas scrubbing [3] or precombustion cap- ture in integrated gasification combined cycles (IGCC) [4]; less mature alternatives include oxy-fuel [5] and chemical looping combustion [6] that involve burning fuel in the absence of atmospheric nitrogen to produce inherently CO 2 - rich flue gas. CO 2 capture may be implemented through retrofit of existing facilities, or alternatively new plants may be built to be capture-ready. The captured CO 2 may then be stored in various sinks, such as inaccessible coal seams, saline aquifers, depleted oil wells [i.e., enhanced oil recovery (EOR)], and other geological reservoirs. In the past decade, interest in CCS as a valuable low-carbon technology has grown significantly and commercial operations are expected within the decade [1, 2]. Full-scale implementation of CCS requires comprehensive process systems engineering techniques to aid in decision making and planning. For example, the grid-wide deploy- ment of carbon capture under steady state assumptions has been addressed by Tan et al. [7, 8] and Pekala et al. [9] using both semigraphical pinch analysis [7] and mathematical pro- gramming techniques [8, 9]. Both these approaches take into consideration the need for new power plants to compensate for energy penalties incurred through retrofit of existing power plants for CCS. Similarly, optimal planning of CO 2 transport infrastructure to match stream sources, such as power plants and industrial facilities, with appropriate sinks or storage sites, is now recognized as an important prerequi- site to successful CCS commercialization [10]. There have been significant efforts to assess CO 2 storage potential in var- ious parts of the world. A notable example is the GeoCapac- ity project in the EU [11]; similar work has also been done in the US [12] and emerging economies such as China and India [13]. At the same time, other researchers have attempted to develop models to provide decision support for CO 2 source– sink matching. For example, Turk et al. [14] made an early attempt to model optimal matching of CO 2 sources and sinks for EOR purposes using a pure integer linear programming model. Subsequently, Benson and Ogden [15] developed a nonlinear optimization model for designing a minimum cost pipeline network, taking into account both the evolution of the network over time and the effect of uncertainties. They also proposed an equivalent dynamic programming formulation of the same problem. More recently, a heuristic algorithm for the design of a CCS pipeline network was Ó 2012 American Institute of Chemical Engineers Environmental Progress & Sustainable Energy (Vol.00, No.00) DOI 10.1002/ep Month 2012 1