Perspective - the need and prospects for negative emission technologies - direct air capture through the lens of current sorption process development

Matthew J. Realff,민윤지,Christopher W. Jones,Ryan P. Lively 한국화학공학회 2021 Korean Journal of Chemical Engineering Vol.38 No.12

We provide a perspective on the development of direct air capture (DAC) as a leading candidate for implementing negative emissions technology (NET). We introduce DAC based on sorption, both liquid and solid, and draw attention to challenges that these technologies will face. We provide an analysis of the limiting mass transfer in the liquid and solid systems and highlight the differences.

Design of biomass processing network for biofuel production using an MILP model

Kim, J.,Realff, M.J.,Lee, J.H.,Whittaker, C.,Furtner, L. Pergamon ; Elsevier Science Ltd 2011 Biomass & bioenergy Vol.35 No.2

This paper presents a general optimization model that enables the selection of fuel conversion technologies, capacities, biomass locations, and the logistics of transportation from the locations of forestry resources to the conversion sites and then to the final markets. A mixed integer linear programming (MILP) model has been formulated and implemented in a commercial software package (GAMS) using databases built in Excel. The MILP represents decisions regarding (1) the optimal number, locations, and sizes of various types of processing plants, (2) the amounts of biomass, intermediate products, and final products to be transported between the selected locations over a selected period, and maximizes the objective function of overall profit. The model has been tested based on an industry-representative data set that contains information on the existing wood resources, final product market locations and demands, and candidate locations and sizes for different types of processing plants, as well as the costs associated with the various processing units and transportation of materials, covering the Southeastern region of the United States. The model is applied to design both a distributed, and a more centralized, conversion system. The overall profits, values, cost, and supply network designs of both systems are analyzed using the optimization model. In particular, we investigate: 1) which parameters have major effect on the overall economics, and 2) the benefits of going to more distributed types of processing networks, in terms of the overall economics and the robustness to demand variations.

An effective chemical pretreatment method for lignocellulosic biomass with substituted imidazoles

Kang, Yuzhi,Realff, Matthew J.,Sohn, Minjeong,Lee, Jay H.,Bommarius, Andreas S. Wiley (John WileySons) 2015 Biotechnology progress Vol.31 No.1

Global evaluation of economics of microalgae-based biofuel supply chain using GIS-based framework

강성환,Matthew J. Realff,Yanhui Yuan,Ronald Chance,이재형 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.6

A microalgae-based biofuel supply chain was designed for different geographic regions, considering thelocal environmental conditions of sunlight, temperature, and available resources of water and CO2. The supply chainwas designed in three distinct areas, Texas, U.S., Northern Territory of Australia, and La Guajira, Colombia, selectedthrough a global analysis of suitable land based on GIS. A three-stage design framework developed in our previousresearch was improved to include a biomass productivity estimation model based on operating data provided by Algenol,a new photobioreactor (PBR) cultivation technology, direct air capture of CO2 as a feedstock option, and functional-unit based optimization. The framework focuses on the comparison of two major cultivation platforms, openraceway pond (ORP) and photobioreactor (PBR) using a net present value metric. A mixed-integer fractional programming(MIFP) model was formulated to make multi-period strategic and tactical decisions related to the supply chaindesign and operation under the objective of minimizing the total cost per gasoline gallon equivalent of products(GGE). Under the same assumptions, the supply chain was designed for seven years and the cost was estimated to be$15.5, $13.5, and $14.0/GGE for the U.S., Colombia, and Australia, respectively. While various processing pathwayswere considered in the model, only a single pathway involving PBR, an algae strain AB1166, and hydrothermal liquefactionwas selected in all regions owing to its cost-efficiency. Direct air capture and hypothetical saline water speciesscenarios were examined to analyze the effect of alternative resource sources on the supply chain design and economics.

Decoking scheduling for industrial naphtha cracking furnaces using a proactive strategy

임희진,최재인,( Matthew Realff ),( Jay H. Lee ),박선원 한국공업화학회 2004 한국공업화학회 연구논문 초록집 Vol.2004 No.1

An Extended Constrained Total Least-Squares Method for the Identification of Genetic Networks from Noisy Measurements

Guner, Ugur,Jang, Hong,Realff, Matthew J.,Lee, Jay H. American Chemical Society 2015 INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH - Vol.54 No.43

<P>We address the system identification problem of genetic networks using noisy and correlated time series data of gene expression level measurements. Least-squares (LS) is a commonly used method for the parameter estimation in the network reconstruction problems. The LS algorithm implicitly assumes that the measurement noise is confined only to the dependent variables. However, a discrete time model for the genetic network systems will lead to serially correlated noise terms that appear in both the dependent and independent variables. A constrained total least-squares algorithm (CTLS) used in signal and image processing applications showed significant improvements in such an estimation problem over the LS and total least-squares (TLS) methods. In this paper, we propose an extended CTLS algorithm that estimates parameters for all the dependent variables simultaneously, instead of estimating them separately for each dependent variable, as in the original CTLS algorithm. In addition, the CTLS algorithm is further generalized to assign weights to the error terms according to the variances or covariances of the measurement noise. We demonstrate its improved performance over the original CTLS method, as well as the commonly used LS and TLS methods on a widely adopted artificial genetic network example, under a variety of noise conditions.</P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ie5b01418'>ACS Electronic Supporting Info</A></P>

A-51 : Decoking scheduling for industrial naphtha cracking furnaces using a proactive strategy

임희진,최재인,( Matthew Realff ),( Jay H Lee ),박선원 한국화학공학회 2007 화학공학의이론과응용 Vol.10 No.2

Continuous operation of naphtha cracking furnace leads to coke formation on the inner surface of the cracking coils. The coke increases with on-stream time and decreases productivity of the furnace. In order to restore productivity, the furnace operation is periodically stopped for decoking. Thus, optimization of the decoking schedule is highly desirable because the decoking sacrifices valuable production time. Measurement errors and unexpected changes in the coke growth rate cause uncertainties. Error accumulation by the uncertainties increases the gap between the furnace model and the real operation. To handle the uncertainties in the coke growth rate and the measurements, a ‘proactive’ scheduling strategy is proposed by extending the previously proposed sequential strategy. Superiority of the proposed proactive scheduling is verified by comparing it with a reactive scheduling strategy and a heuristic policy. (Acknowledgement: This work was supported by the BK21 Project, the IMT2000(project number: 00015993) in 2003, and Center for Ultramicrochemical Process Systems sponsored by KOSEF)

Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty

Shin, Joohyun,Lee, Jay H.,Realff, Matthew J. Elsevier 2017 APPLIED ENERGY Vol.195 No.-

<P>Distributed and on-site energy generation and distribution systems employing renewable energy sources and energy storage devices (referred to as microgrids) have been proposed as a new design approach to meet our energy needs more reliably and with lower carbon footprint. Management of such a system is a multi-scale decision-making problem encompassing hourly dispatch, daily unit commitment (UC), and yearly sizing for which efficient formulations and solution algorithms are lacking thus far. Its dynamic nature and high uncertainty are additional factors in limiting efficient and reliable operation. In this study, two-stage stochastic programming (2SSP) for day-ahead UC and dispatch decisions is combined with a Markov decision process (MDP) evolving at a daily timescale. The one-day operation model is integrated with the MDP by using the value of a state of commitment and battery at the end of a day to ensure longer term implications of the decisions within the day are considered. In the MDP formulation, capturing daily evolving exogenous information, the value function is recursively approximated with sampled observations estimated from the daily 2SSP model. With this value function capturing all future operating costs, optimal sizing of the wind farm and battery devices is determined based on a surrogate function optimization. Meanwhile, a multi-scale wind model consistent from seasonal to hourly is developed for the connection of the decision hierarchy across the scales. The results of the proposed integrated approach are compared to those of the daily independent 2SSP model through a case study and real wind data. (C) 2017 Elsevier Ltd. All rights reserved.</P>

Machine learning: Overview of the recent progresses and implications for the process systems engineering field

Lee, Jay H.,Shin, Joohyun,Realff, Matthew J. Elsevier 2018 Computers & chemical engineering Vol.114 No.-

<P><B>Abstract</B></P> <P>Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Recent advances in deep learning and reinforcement learning (RL) are reviewed. </LI> <LI> Motivation, early problems and recent resolutions of deep learning are discussed. </LI> <LI> The idea of RL and its success in the Go game (<I>a la</I> AlphaGo) are introduced. </LI> <LI> Applicability of RL to multi-stage decision problems in industries is discussed. </LI> <LI> Potential applications and research directions of ML in the PSE domains are given. </LI> </UL> </P>

가상수요곡선을 이용한 일정계획법

Min-Gu Kang,Jay H. Lee,Matthew J. Realff,Sunwon Park 제어로봇시스템학회 2005 제어로봇시스템학회 국내학술대회 논문집 Vol.2005 No.10