M.M. Faruque Hasan

Municipal solid waste to liquid transportation fuels – Part I: Mathematical modeling of a municipal solid waste gasifier

Abstract This paper presents a generic gasifier model towards the production of liquid fuels using municipal solid waste (MSW) as a feedstock. The MSW gasification has been divided into three zones: pyrolysis, oxidation, and reduction. The pyrolysis zone has been mathematically modeled with an optimization based monomer model. Then, the pyrolysis, oxidation, and reduction zones are defined with different chemical reactions and equations in which some extents of these reactions are not known a priori. Using a nonlinear parameter estimation approach, the unknown gasification parameters are obtained to match the experimental gasification results in the best possible way. The results suggest that a generic MSW gasifier mathematical model can be obtained in which the average error is 8.75%. The mathematical model of the MSW gasifier is of major importance since it can be a part of a process superstructure towards the production of liquid transportation fuels.

A multi-scale framework for CO 2 capture, utilization, and sequestration: CCUS and CCU

Abstract We present a multi-scale framework for the optimal design of CO 2 capture, utilization, and sequestration (CCUS) supply chain network to minimize the cost while reducing stationary CO 2 emissions in the United States. We also design a novel CO 2 capture and utilization (CCU) network for economic benefit through utilizing CO 2 for enhanced oil recovery. Both the designs of CCUS and CCU supply chain networks are multi-scale problems which require decision making at material, process and supply chain levels. We present a hierarchical and multi-scale framework to design CCUS and CCU supply chain networks with minimum investment, operating and material costs. While doing so, we take into consideration the selection of source plants, capture processes, capture materials, CO 2 pipelines, locations of utilization and sequestration sites, and amounts of CO 2 storage. Each CO 2 capture process is optimized, and the best materials are screened from large pool of candidate materials. Our optimized CCUS supply chain network can reduce 50% of the total stationary CO 2 emission in the U.S. at a cost of

Municipal solid waste to liquid transportation fuels – Part II: Process synthesis and global optimization strategies

35.63 per ton of CO 2 captured and managed. The optimum CCU supply chain network can capture and utilize CO 2 to make a total profit of more than 555 million dollars per year (

Modeling and simulation of main cryogenic heat exchanger in a base-load liquefied natural gas plant

9.23 per ton). We have also shown that more than 3% of the total stationary CO 2 emissions in the United States can be eliminated through CCU networks at zero net cost. These results highlight both the environmental and economic benefits which can be gained through CCUS and CCU networks. We have designed the CCUS and CCU networks through (i) selecting novel materials and optimized process configurations for CO 2 capture, (ii) simultaneous selection of materials and capture technologies, (iii) CO 2 capture from diverse emission sources, and (iv) CO 2 utilization for enhanced oil recovery. While we demonstrate the CCUS and CCU networks to reduce stationary CO 2 emissions and generate profits in the United States, the proposed framework can be applied to other countries and regions as well.

Optimizing Compressor Operations in an LNG Plant

Abstract This paper investigates the production of liquid transportation fuels from municipal solid waste (MSW). A comprehensive process synthesis superstructure is utilized that incorporates a novel mathematical model for MSW gasification. The production of liquid products proceeds through a synthesis gas intermediate that can be converted into Fischer–Tropsch hydrocarbons or methanol. The methanol can be converted into either gasoline or olefins, and the olefins may subsequently be converted into gasoline and distillate. Simultaneous heat, power, and water integration is included within the process synthesis framework to minimize utilities costs. A rigorous deterministic global optimization branch-and-bound strategy is utilized to minimize the overall cost of the waste-to-liquids (WTL) refinery and determine the optimal process topology. Several case studies are presented to illustrate the process synthesis framework and the nonconvex mixed-integer nonlinear optimization model presented in this paper. This is the first study that explores the possibility of liquid fuels production from municipal solid waste utilizing a process synthesis approach within a global optimization framework. The results suggest that the production of liquid fuels from MSW is competitive with petroleum-based processes. The effect that the delivered cost of municipal solid waste has on the overall cost of liquids production is also investigated parametrically.

Systematic process intensification using building blocks

Abstract Recent growth in world-wide consumption of natural gas highlights its immense importance as a source of primary energy. Liquefied natural gas (LNG) is the most economic way to transport natural gas over long distances. Main Cryogenic Heat Exchanger (MCHE) is a very critical equipment in an energy intensive LNG plant. To that end, modeling MCHE is the inevitable first step in the optimization of LNG plant operation. In this paper, we develop a model that is designed to simulate and predict the performance of an existing MCHE without knowing its physical details. The concept of superstructure representation is employed to derive an equivalent 2-stream heat exchanger network. The objective is to address the rating of an existing MCHE or the prediction of its performance rather than finding the area for a design or minimizing the cost. We use a mixed-integer nonlinear programming (MINLP) approach to select the best network that describes an existing MCHE. An example case is also presented to assess the ability of our model in predicting the performance of a MCHE.

Optimization of Compressor Networks in LNG Operations

Abstract Global concern about energy, ecology and environment strongly suggests that energy should be utilized efficiently. Although liquefied natural gas (LNG) is an attractive source of clean fossil fuel, it involves energy intensive liquefaction. Moreover, compressors are often operated in suboptimal fashion in the process, which results in higher energy consumption. The APCI propane precooled mixed refrigerant process is most widely used process in base-load LNG plants. To this end, we optimize the compressor operations for the twin refrigeration cycles of the APCI process. The objective here is to minimize the total power cost for the refrigerant compressors. We identify that the propane pressure in the propane precooling cycle plays a role for the power consumption in the compressors and hence use it as a decision variable. Without much loss of generality, this model could be also used for other LNG processes. Finally, we present a case study on an existing LNG plant model.

Surrogate-based VSA Process Optimization for Post-Combustion CO2 Capture

Abstract We present a novel method for systematic process design and intensification. We depart from the classical unit operation-based representation of process units, flowsheets and superstructures and propose a new representation using fundamental building blocks. These building blocks can be associated with different process phenomena, tasks and unit operations. An assembly of blocks of the same type obtains a classical unit, while an assembly of blocks with different types results in an intensified unit. This allows to systematically identify and incorporate many intensification pathways using a general block-based superstructure. We design an intensified process by optimizing a performance metric for given raw materials and product specifications, material properties and bounds on flow rates. The overall problem is formulated using a single mixed-integer nonlinear optimization (MINLP) model that can be solved using commercial solvers. We show the applicability of our approach using several design and intensification case studies.

A multi-scale approach for the discovery of zeolites for hydrogen sulfide removal

Liquefied natural gas (LNG) is the most economic way of transporting natural gas (NG) over long distances. Although LNG is an attractive source of clean fossil fuel, it involves energy intensive liquefaction of NG using refrigeration. Often the compressors that run the refrigerant cycles in an LNG plant operate in suboptimal fashion, which results in higher fuel and energy consumption. To this end, we present a generalized model for the compressor operations in multiple interacting refrigerant cycles in LNG and other cryogenic applications. We determine the optimal load distribution between the cycles to minimize total power consumption of the system for a given plant capacity and operating conditions. We also show the applicability of our model using a case study on the AP-XTM LNG process, which includes three interacting cycles, each with single or multiple compressors.

Optimal synthesis of periodic sorption enhanced reaction processes with application to hydrogen production

Abstract Post-combustion CO 2 capture in existing power plants is essential to arrest the current rise in atmospheric CO 2 and the consequent alarming trend of global warming. While absorption and pressure swing adsorption are well-known carbon capture technologies, vacuum swing adsorption (VSA) is a potential candidate. In this work, a comprehensive non-isothermal model is first developed and implemented in the multi-physics software COMSOL to simulate various modes of VSA operation. Our extensive parametric study suggests that even a simple basic VSA cycle can capture CO 2 with high purity & recovery at comparable or lower energy penalty than published data. The rigor of the full transient VSA simulations to reach the cyclic steady state, however, make fully rigorous VSA optimization intractable. To this end, we present a sequential optimization strategy based on response surface models with synergistic combination of COMSOL simulation model with Design and Analysis of Computer Experiments (DACE). Unlike most optimization studies which either focus on maximizing CO 2 purity/recovery or minimizing energy penalty, we use the total-ownership-of-cost approach to rationally drag technology performance, technology economics, energy penalty and environmental impacts to a single basis (