As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hyd... [more]

Thermal separation processes, such as distillation, play a pivotal role in the chemical and petrochemical sectors, constituting a substantial portion of the industrial energy consumption. Consequently, owing to their huge application scales, these processes contribute significantly to greenhouse gas (GHG) emissions. Decarbonizing distillation units could mitigate carbon emissions substantially. Heat Pumps (HP), that recycle lower quality heat from the condenser to the reboiler by electric work present a unique opportunity to electrify distillation systems. In this research we try to answer the following question in the context of multi-component distillation – Do HPs actually reduce the effective fuel consumption or just merely shift the fuel demand from chemical industry to the power plant? If they do, what strategies consume minimum energy? To address these inquiries, we construct various simplified surrogate and shortcut models designed to effectively encapsulate the fundamental phy... [more]

With growing concern over the effects of green-house gas emissions, there has been an increase in emission-reducing policies by governments around the world, with over 70 countries having set net-zero emission goals by 2050-2060. These are ambitious goals that will require large investments into the expansion of renewable and low-carbon technologies. The decisions about which technologies should be invested in can be difficult to make since they are based on information about the future, which is uncertain. When considering emerging technologies, a source of uncertainty to consider is how the costs will develop over time. Learning curves are used to model the decrease in cost as the total installed capacity of a technology increases. However, the extent to which the cost decreases is uncertain. To address the uncertainty present in multiple aspects of the energy sector, multistage stochastic programming is employed considering both exogenous and endogenous uncertainties. It is observed... [more]

Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat and electricity coupled with a high temperature steam electrolyzers (HTSE) for H2 production. The ability to co-produce H2 along with nuclear is widely seen as critical to improving the economics of nuclear energy technologies. To that end, our model focuses on evaluating the least-cost design and operations of the NPP-HTSE system while accounting for: a) power consumption variation with current den... [more]

Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC syste... [more]

As the United States continues efforts to decarbonize the power and transportation sectors, significant challenges associated with the reliance of clean energy technologies on rare earth elements (REEs) will have to be overcome. One potential approach for increasing the supply of these elements is to extract REEs from end-of-life (EOL) hard disk drives (HDDs). HDDs contain neodymium and praseodymium, which are among the most important REEs for the clean energy transition, as they are crucial to producing the permanent magnets needed for wind turbines and electric vehicles. Here, we propose a superstructure-based approach to find the optimal pathway for recovering REEs from EOL HDDs. The superstructure was optimized by maximizing the net present value (NPV) over 15 years. Projected prices for commercial rare earth oxides and the projected amount of EOL HDDs in the U.S. were estimated and used in the model. These projections were used to establish the base case optimal result, assuming t... [more]

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-economic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES®-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully gene... [more]

A novel process system which integrates an isopropanol-based chemical heat pump with a post-combustion carbon capture unit was proposed, designed, and analyzed. The system uses low-quality waste heat (~80°C) produced through the CO2 adsorption step of a carbon capture process and upgrades that heat to a higher temperature (~150°C) using the chemical heat pump. The chemical heat pump is powered mostly by the waste heat and requires only a small amount of electricity. The higher temperature heat produced can be used in the desorption stage of the CO2 capture process, displacing a portion of the existing fossil energy required. The energy and exergy performance characteristics of the chemical heat pump were computed using the results of a steady state simulation in a systems analysis. Using exergy cost correlations, the profitability of the chemical heat pump concept was estimated. It was found that for this particular configuration, the fossil energy load of desorption could be reduced b... [more]

This research is dedicated to designing and economically evaluating the green ammonia supply chain, considering the fluctuating nature of renewable energy sources and energy demand across both hourly and seasonal variations. It also explores the impact of economies of scale and the delays associated with long-distance shipping to meet energy demands in a timely manner. These considerations require the formulation of a Mixed-Integer Nonlinear Programming model, further complicated by the necessity for a two-stage stochastic programming approach. We introduce a hierarchical optimization framework that utilizes a decomposition method to differentiate between one-time design decisions and subsequent operational choices. At the upper level, potential design solutions are identified through the Bayesian Optimization and Hyperband algorithm, which effectively navigates the non-linear challenges posed by economies of scale. The lower level then addresses a Mixed-Integer Linear Programming prob... [more]

This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs, and the optimal sites for hydrogen production plants. The second stage delves into each selected hub, analyzing energy mixes under variable solar, wind, and grid profiles, sizing specific production and storage facilities, and scheduling to match energy availability. Iterative refinement incorporates detailed insights back into the broader model, updating costs and configurations to converge upo... [more]

The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, storage and dispatch, (ii) synthesis of polymeric materials, (iii) manufacturing of vehicles and establishment of infrastructure. Decision-making frameworks that can coordinate these aspects and provide cooperative sustainable solutions are needed. To this end, we present a multiscale modeling and optimization framework for the simultaneous resolution of the material and energy value chains. A case... [more]

The decarbonization of the society has a very high effect on the power grids as especially the energy generation will be almost completely shifted to CO2-neutral sources such as wind and solar. This implies significant design changes to the power grids and power systems, which lie between the electricity producers and consumers. In this paper, we discuss both the generation and consumer side, including the grid changes and required data exchange to support the transition.

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LC... [more]

Biogas is a promising energy source for sustainable hydrogen production due to its high concentration of CH4. However, determining the optimal process configuration is challenging due to the uncertainty of the fed biogas composition and the sensitivity of the operating conditions. This necessitates early-stage evaluation of the biomass-to-hydrogen process's performance, considering economics, energy efficiency, and environmental impacts. A data-driven model was introduced for early-stage assessment of hydrogen production from biogas without whole process simulation and optimization. The model was developed based on various biogas compositions and generated parameters for mass and energy balance. A database of unit processes was created using simulation models. Sensitivity analysis was performed under four techno-economic and environmental evaluation criteria: Unit Production Cost (UPC), Energy Efficiency (EEF), Net CO2 equivalent Emission (NCE), and Maximum H2 Production (MHP). The ea... [more]

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

The chemical industry is actively pursuing energy transition and decarbonization through renewables and other decarbonization initiatives. However, navigating this transition is challenging due to uncertainties in capital investments, electricity costs, and carbon taxes. Adapting to decarbonization standards while preserving existing valuable infrastructure presents a dilemma. Early transitions may lead to inefficiencies, while delays increase the carbon footprint. This research proposes a framework to find an optimal retrofit decarbonization strategy for existing oil refineries. We start with a generic process flowsheet representing the refinery's current configuration and operations, and consider various decarbonization alternatives. Through superstructure optimization, we identify the most cost-effective retrofit strategy over the next three decades to achieve decarbonization goals. We develop a Mixed-Integer Linear Programming (MILP) model, integrating simplified process equations... [more]

The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice o... [more]

Chemical looping ammonia synthesis (CLAS) is a new ammonia synthesis method capable of efficiently synthesizing ammonia at atmospheric pressure. The low-pressure operation of CLAS systems could decrease the capital and operational costs of ammonia synthesis. Despite its early developmental stage, the use of standard process engineering equipment in CLAS makes it possible to reasonably assess its economic potential. In this study, we evaluated the technoeconomic potential of CLAS systems in comparison to a Haber-Bosch (HB) synthesis process in the context of green ammonia production. CLAS is more compatible with the separate nitrogen and hydrogen feedstocks used in green ammonia production, and cost savings from CLAS could improve the economic viability of green ammonia production. Ammonia synthesis loops were modeled in Aspen Plus and the levelized cost of ammonia (LCOA) of each system was calculated. Three CLAS systems; two high temperature and one low-temperature chemical loop, were... [more]

Direct air capture (DAC) of carbon dioxide is a promising technology to enable climate change mitigation. The liquid solvent DAC (LSDAC) process is one of the leading technologies being piloted. However, LSDAC uses a high-temperature regeneration process which requires a lot of thermal energy. Although current LSDAC designs incorporate pre-heat cyclones and a heat recovery steam generator to enable heat recovery, these do not maximize the use of the heat in the products of calcination. In this paper, a linear optimization model is developed to minimize energy cost in a LSDAC that is powered by renewable energy and natural gas. First, the material flow network is modified to include a heat exchanger (HX) and water supply to a proton exchange membrane (PEM) electrolyser. Mass and energy balance constraints are then developed to include the water flow as well as the energy balance at the PEM and the HX. Results show that about 911 tonnes of hydrogen could be produced over 336 hours of ope... [more]

Water-gas shift membrane reactors (WGS-MRs) offer a pathway to affordable blue H2 generation/purification from gasified feedstock or reformed fuels. To exploit their cost benefits for blue hydrogen production, WGS-MRs’ performance needs to be optimized, which includes navigating the multidimensional design space (e.g., temperature, feed pressures, space velocity, membrane permeance and selectivity, catalytic performance). This work describes an equation-oriented modeling framework for WGS-MRs in the Pyomo ecosystem, with an emphasis on model scaling and multi-start initialization strategies to facilitate reliable convergence with nonlinear optimization solvers. We demonstrate, through sensitivity analysis, that our model converges rapidly (< 1 CPU second on a laptop computer) under a wide range of operating parameters (e.g., feed pressures of 1-3 MPa, reactor temperatures of 624-824 K, sweep-to-feed ratios of 0-0.5, and steam/carbon ratios of 1-5). Ongoing work includes (1) validat... [more]

In this work, we present a follow-up work of reinforcement learning (RL)-driven process design using the Institute for Design of Advanced Energy Systems Process Systems Engineering (IDAES-PSE) Framework. Herein, process designs are generated as stream inlet-outlet matrices and optimized using the IDAES platform, the objective function value of which is the reward to RL agent. Deep Q-Network is employed as the RL agent including a series of convolutional neural network layers and fully connected layers to compute the actions of adding or removing any stream connections, thus creating a new process design. The process design is then informed back to the RL agent to refine its learning. The iteration continues until the maximum number of steps is reached with feasible process designs generated. To further expedite the RL search of the design space which can comprise the selection of any candidate unit(s) with arbitrary stream connections, we investigate the role of RL reward function and... [more]

Pharmaceutically elegant tablets are an expectation from pharmacists, health care providers and consumers for solid oral dosage forms. The presence of non-aesthetically pleasing defects in solid oral dosage forms can result in complaints back to the manufacturer and potentially non-compliance with medicines. The purpose of this study was to simulate and analyze the design of a tablet core and the aqueous film-coating process, to gain a better understanding of tablet defect generation, and to help eliminate the defects from the finished product. This evaluation employs Discrete Element Method (DEM) using the software product Altair® EDEM™ to understand the potential mechanisms that are causing the defects, based on the forces tablets experience in the coating operation, along with the number of tablet-to-tablet interactions that occur during the duration of the process. Defects observed during the scale up of the coating process to a commercial production scale confirmed the DEM results... [more]

The integrated design and control (IDC) framework is becoming increasingly important for systematic design of flexible manufacturing and energy systems. Recent advances in computing and derivative-free optimization have enabled more tractable solution methods for complex IDC problems that involve, e.g., multi-period dynamics, the presence of high-variance and non-stationarity probabilistic uncertainties, and mixed-integer control/scheduling decisions. Parallelly, developments in techno-ecological synergy (TES) have allowed co-design of industrial and environmental systems that have been shown to lead to win-win solutions in terms of the economy, ecological, and societal benefits. In this work, we propose to combine the IDC and TES frameworks to more accurately capture the real-time interactions between process systems and the surrounding natural resources (e.g., forests, watersheds). Specifically, we take advantage of (multi-scale) model predictive control to close the loop on a realis... [more]

Copolymers are commonplace in various industries. Nevertheless, fine-tuning their properties bears significant cost and effort. Hence, an ability to predict polymer properties a priori can significantly reduce costs and shorten the need for extensive experimentation. Given that the physical and chemical characteristics of copolymers are correlated with molecular arrangement and chain topology, understanding the reactivity ratios of monomers—which determine the copolymer composition and sequence distribution of monomers in a chain—is important in accelerating research and cutting R&D costs. In this study, the prediction accuracy of two Artificial Neural Network (ANN) approaches, namely, Multi-layer Perceptron (MLP) and Graph Attention Network (GAT), are compared. The results highlight the potency and accuracy of the intrinsically interpretable ML approaches in predicting the molecular structures of copolymers. Our data indicates that even a well-regularized MLP cannot predict the reacti... [more]

Continuous manufacturing in pharmaceutical industries has shown great promise to achieve process intensification. To better understand and justify such changes to the current status quo, a technoeconomic analysis of a continuous production must be conducted to serve as a predictive decision-making tool for manufacturers. This paper uses PharmaPy, a custom-made Python-based library developed for pharmaceutical flowsheet analysis, to simulate an annual production cycle for a given active pharmaceutical ingredient (API) of varying production volumes for a batch crystallization system and a continuous mixed suspension, mixed product removal (MSMPR) crystallizer. After each system is optimized, the generalized cost drivers, categorized as capital expenses (CAPEX) or operational expenses (OPEX), are compared. Then, a technoeconomic and sustainability cost analysis is done with the process mass intensity (PMI) as a green metric. The results indicate that while the batch system does have an ov... [more]