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Continuous reactor network design for rigid polyol production

CHEMICAL ENGINEERING SCIENCE 2021年 230卷 116189-116189页

作者： Wen, Yunhan Biegler, Lorenz. T. Ochoa, Maria P. Matthews, Logan Ferrio, Jeff Weston, John Nikbin, Nima Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Dow Chem Co USA Midland MI 48674 USA

In this study, we focus on developing continuous reactor network models able to produce rigid polyols under strict product and safety specifications. At the same time, we determine the optimal decision profiles that lead to minimum capital cost. Decision variables include the feed rates of initiator, monomers and catalyst, reactor temperature, residence time, number and location of monomer injection points. Moreover, we narrow down the types of continuous reactors that can be part of the network to two: plug flow reactor (PFR) with multiple feed injection points and continuous stirred tank reactor (CSTR). The PFR model is a differential algebraic equation (DAE) optimization problem. The simultaneous collocation method is applied to transform the DAE into a mixed integer nonlinear programming (MINLP) problem. An iterative algorithm is proposed to solve the MINLP, where binary variables are manually fixed. The result of this strategy shows that the reactor configuration with minimum capital cost is a PFR with ten monomer injection points. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Polymer reactors Reactor network synthesis Process optimization mixed integer nonlinear programming

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Interactive nonlinear multiobjective optimal design of water distribution systems using Pareto navigator technique

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SUSTAINABLE CITIES AND SOCIETY 2021年 73卷 103110-103110页

作者： Moazeni, Faegheh Khazaei, Javad Penn State Harrisburg Sch Sci Engn & Technol Civil & Environm Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Harrisburg Elect Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Univ Pk Architectural Engn Dept State Coll PA 16801 USA

Sustainable water distribution systems comprise complex combinations of critical components that are simultaneously optimized. The trade-offs and interactions that exist among these components, however, will introduce difficult challenges to optimization algorithms. In this paper, a multiobjective optimization formulation is proposed to scrupulously manage the demand, leakage, and age of water, as well as energy consumption of water distribution systems. Pareto navigator approach, a learning-oriented algorithm, is used to attain the final Pareto optimal solution for water network. The optimal power consumption, leakage, demand, and water age are used as inputs to the upper level energy resource allocation to optimize the energy demand and operational cost of integrated water-energy system. Numerical results show 76.6%, 95.4%, 13.14%, and 27.4% reduction in leakage, water age, water usage, and energy consumption of water network over 24 h, respectively, while the daily cost of electricity generation reduces by 3.7%. The energy system is designed based on isolated energy units, also known as microgrids, with minimum dependency on the fossil-fuel-based power plants. As such, the proposed multiobjective bi-level optimization model will also improve the carbon footprint of water distribution systems and their impact on climate change.

关键词： Multiobjective optimization Pareto optimal solution Water-energy nexus Bi-level optimization model mixed integer nonlinear programming Sustainable systems

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Superstructure design and optimization on supercritical carbon dioxide cycle for application in concentrated solar power plant

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ENERGY CONVERSION AND MANAGEMENT 2020年第0期206卷 112290-000页

作者： Ma, Yuegeng Morosuk, Tatiana Luo, Jing Liu, Ming Liu, Jiping Xi An Jiao Tong Univ MOE Key Lab Thermal Fluid Sci & Engn Xianning West Road 28 Xian 710049 Peoples R China Tech Univ Berlin Inst Energy Engn Marchstr 18 D-10587 Berlin Germany Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xianning West Road 28 Xian 710049 Peoples R China

A superstructure-based method is applied to optimize the design of supercritical carbon dioxide cycle for concentrated solar power systems. A superstructure is designed for the cycle featuring various options in cold-end and hot-end configurations. Process integration and cycle variables are simultaneously optimized for the superstructure based on the integrated thermoeconomic model of the overall system. Optimizations are performed for eight cases with different designs of maximum and minimum temperatures. Sensitivity analyses are conducted on the design parameters and related capital costs of subsystems of the overall system. 1.5-13.5% reduction in levelized cost of electricity is obtained through the superstructure-based optimization relative to the corresponding base cases. The cost of thermal storage system at optimal condition is significantly surged by 36.3-111.6% when the maximum temperature surpasses 600 degrees C, which consequently leads to the variation in configuration for the optimal cycle. The minimum levelized cost of electricity is obtained at 600 degrees C with a reduction of up to 4.1% compared to that at 550 degrees C. The further increase in the maximum temperature beyond 600 degrees C will be followed by 8.6%-0.2% rise in the levelized cost of electricity. The improvements in maximum pressure and turbomachinery performance can lead to significant reduction in levelized cost of electricity, especially at high maximum and minimum temperatures. However, these improvements have relatively uneventful effects on the configuration of the optimal cycle. The capital cost of thermal storage system has significant effects on the design of the optimal cycle configuration whereas the variation in the capital cost of cycle system has less eventful effects. The effect of capital cost of cycle system is more dominant than that of the thermal storage system at 550 degrees C and 600 degrees C maximum temperatures in terms of levelized cost of electricity, while

关键词： Concentrated solar power plant Supercritical carbon dioxide cycle Superstructure design mixed integer nonlinear programming Optimization

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Process synthesis and economic analysis of cyanobacteria biorefineries: A superstructure-based approach

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APPLIED ENERGY 2019年 253卷 113625-000页

作者： Fasahati, Peyman Wu, Wenzhao Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

In this study, we develop, synthesize, and evaluate a cyanobacteria biorefinery incorporating all relevant subsystems including: (1) supply of CO2, water, and nutrients, (2) production at cultivation ponds, and (3) products separation and purification. A superstructure-based approach is proposed to evaluate technical and economic feasibility of cyanobacteria biorefineries to produce biochemicals. Supply subsystems include a wastewater treatment (WWT) facility and a power plant to provide water, nutrients, and CO2. In the production subsystem, an approach to model cultivation ponds is developed based on production requirements and cyanobacteria characteristics i.e. residence time and productivity. In the purification subsystem, optimal separation technologies are selected based on product attributes and concentration to recover products and recycle water and nutrients, while minimizing costs. Later, all subsystems are integrated and system-wide optimization is performed. Impacts of key parameters such as cyanobacteria productivity and performance for a cellular level analysis;and CO2 transportation phase, distances, and product titers for a systems level analysis are investigated. Results indicate that depending on product attributes, the production cost could vary between $2.74-$34/kg ($0.7-$2.65/kg) at product concentrations of 0.5 g/L (10 g/L), highlighting the importance of achieving higher product titers. Analyses for the base-case show that for a titer of 0.5 g/L, reducing productivity from 30 to 5 gm(-2) day(-1) while increasing residence time from 5 to 30 days increases the production cost by about 22% in the system with water and nutrients supply form WWT, respectively. Results show that providing water and nutrients from a WWT facility improves the economics. The present study provides critical insights for future research and development and successful commercialization of cyanobacteria biorefineries.

关键词： Microalgae Optimization mixed integer nonlinear programming Biochemicals Biofuels

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A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations

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ENERGIES 2019年第12期12卷 2252-2252页

作者： San Juan, Jayne Lois G. Aviso, Kathleen B. Tan, Raymond R. Sy, Charlle L. De La Salle Univ Ind Engn Dept Manila 0922 Philippines De La Salle Univ Chem Engn Dept Manila 0922 Philippines De La Salle Univ Ctr Engn & Sustainable Dev Res Manila 0922 Philippines

The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.

关键词： biomass co-firing biomass quality network optimization goal programming mixed integer nonlinear programming

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Co-optimization of wastewater treatment plants interconnected with smart grids

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APPLIED ENERGY 2021年 298卷 117150-117150页

作者： Moazeni, Faegheh Khazaei, Javad Penn State Harrisburg Civil Engn Dept Sch Sci Engn & Technol 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Harrisburg Elect Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Univ Pk Architectural Engn Dept State Coll PA 16801 USA

Wastewater treatment plants (WWTPs) and smart grids of urban systems are in a strong nexus. Energy consumption of WWTPs will directly impact the economy of the smart grids, while the energy forecast, marketing, and resource allocation of smart grids will affect the economy of WWTPs. As such, these two critical infrastructures need to be operated and managed cooperatively to maximize the mutual economic benefits. In this paper, a mixed integer nonlinear programming (MINLP) co-optimization model is developed to address the day-ahead economic dispatch problem of smart grids embedded with interdependent links among wastewater treatment plants and smart grids. The energy demand of WWTPs is optimized by minimizing the energy consumption of the aeration module, mixed liquor pumping, primary clarifier's influent pumping, secondary clarifier's sludge pumping, and mixing devices that are integrated in the dynamic economic dispatch problem of smart grid. Additionally, biochemical oxygen demand (BOD) and total Kjedhal nitrogen (TKN) concentrations of the treated wastewater is also incorporated in the economic dispatch problem to ensure of the high quality of the WWTP's effluent. Electricity consumption of the buildings is also added as a constraint to the economic dispatch, serving as a dynamic load for smart grid. Case studies are conducted to examine the impact of wastewater flow rate, influent BOD and TKN concentrations, battery efficiency, and end-of-day battery's state of the charge constraints on economic dispatch of the smart grid. The results show that total operational cost and energy consumption of the integrated WWTP-smart grid is increased only by 3.4% and 1.75%, respectively, as the daily influent flow rate and BOD & TKN concentrations are increased from their minimum to maximum thresholds.

关键词： Economic dispatch formulation Demand response mixed integer nonlinear programming Critical infrastructure Energy management Resource allocation

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Optimal energy management of water-energy networks via optimal placement of pumps-as-turbines and demand response through water storage tanks

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APPLIED ENERGY 2021年 283卷 116335-116335页

A sustainable water infrastructure cannot be made possible without improving its energy efficiency in terms of the consumption value and the type of supplying resources. One solution to reduce the external energy requirement of the water systems is to decrease the energy consumption, while the internal energy recovery capacity is increased. Such is achieved by the present work through (1) optimizing the water demand and tanks' and pumps' scheduling and operations and (2) installing pumps-as-turbines (PATs) where there is excess pressure head in the water. In this paper, two optimization models are developed. The first optimization problem is designed to find the optimal number and location of the PATs among the eligible positions across a water system. The optimal solution will be incorporated in the second optimization problem, i.e., an economic dispatch problem, where the cost of energy generation of an integrated water-energy system is minimized while the water and electrical load demands, as well as the hydraulic integrity of the system are satisfied. The energy system consists of conventional power generation units aggregated of natural gas and diesel based generators, solar photovoltaic units, wind generators, and battery energy storage systems. The proposed models are verified with case studies, and the numerical results show more than 30% reduction in the daily electricity consumption of water systems and $79 in daily operating cost of energy generation in water-energy systems when 3 PATs were installed at optimal locations.

关键词： Pumps-as-turbines Water distribution network Water-energy nexus Economic dispatch formulation Demand response mixed integer nonlinear programming

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A Multi-Objective Life Cycle Optimization Model of an Integrated Algal Biorefinery toward a Sustainable Circular Bioeconomy Considering Resource Recirculation

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ENERGIES 2021年第5期14卷 1416页

作者： Solis, Celine Marie A. San Juan, Jayne Lois G. Mayol, Andres Philip Sy, Charlle L. Ubando, Aristotle T. Culaba, Alvin B. De La Salle Univ Ind Engn Dept Manila 0922 Philippines De La Salle Univ Ctr Engn & Sustainable Dev Res Manila 0922 Philippines De La Salle Univ Mech Engn Dept Manila 0922 Philippines

Biofuel production from microalgae biomass has been considered a viable alternative to harmful fossil fuels;however, challenges are faced regarding its economic sustainability. Process integration to yield various high-value bioproducts is implemented to raise profitability and sustainability. By incorporating a circular economy outlook, recirculation of resource flows is maximized to yield economic and environmental benefits through waste minimization. However, previous modeling studies have not looked into the opportunity of integrating productivity reduction related to the continuous recirculation and reuse of resources until it reaches its end of life. In this work, a novel multi-objective optimization model is developed centered on an algal biorefinery that simultaneously optimizes cost and environmental impact, adopts the principle of resource recovery and recirculation, and incorporates the life cycle assessment methodology to properly account for the environmental impacts of the system. An algal biorefinery involving end-products such as biodiesel, glycerol, biochar, and fertilizer was used for a case study to validate the optimization model. The generated optimal results are assessed and further analyzed through scenario analysis. It was seen that demand fluctuations and process unit efficiencies have significant effect on the optimal results.

关键词： algal biofuel algal biorefinery life cycle optimization mixed integer nonlinear programming

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A game theory based demand-side management in a smart microgrid considering price-responsive loads via a twofold sustainable energy justice portfolio

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期52卷

作者： Rezaei, Navid Meyabadi, Abbas Fattahi Deihimi, Mohammadhossein Univ Kurdistan Dept Elect Engn Sanandaj Iran Hamedan Univ Technol Dept Elect Engn Hamadan Hamadan Iran

This paper introduces a demand-side integration (DSI) framework that upholds the efficacious electrical energy consumption to attain the aims of smart grid as well as the customers' requirement. The proposed DSI framework is based on a pre-paid orderly energy consumption strategy for a smart microgrid. The interaction between aggregators and end-use customers is captured as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to derive the profitable daily electricity tariffs considering an elasticity-based model of price responsive load. The resources caused by proper response of small-scale consumers are integrated into day ahead energy scheduling problem. In order to cope the real-time deviations, the DSI program is accompanied by a supplementary pay-off module. The DSI framework is formulated as a stochastic optimization problem in the form of mixed integer nonlinear programming involving probabilistic representation of uncertainty in generation pattern of renewable energy resources. According to the simulation results, in contrast with the normal consumption paradigm, the load factor improves at least 1.36%, the net profit of unified entity enhances 0.27%, and the total gas emissions are mitigated about 10.9%. The outcomes demonstrate the accuracy and merit of the proposed method.

关键词： Demand-side integration mixed integer nonlinear programming Nash Bargaining Game Pre-paid Energy Consumption Smart microgrid

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Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing

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Energy Procedia 2017年 142卷 1953-1959页

作者： Maomao Hu Fu Xiao Department of Building Services Engineering The Hong Kong Polytechnic University Kowloon Hong Kong

Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residential air conditioner (ACs) focus on the single-speed ACs, rather than the inverter-driven ACs which are more energy efficient and extensively installed in today’s residential buildings. This paper presents a novel model-based optimal load scheduling method for residential inverter-driven ACs to realize automatic DR to the day-ahead dynamic electricity prices. The models of the inverter-driven ACs and room thermal dynamics are firstly developed, identified and integrated for the development of the model-based scheduling. The tradeoff problem between the electricity costs, resident’s comfort and peak power reductions is formulated as a mixed-integer nonlinear programming problem with adjustable weightings. The optimal solution of the nonlinear programming problem is searched by the genetic algorithm (GA). Simulation results show that multiple goals can be achieved via GA optimization and the regulations of the weights in the objective function. The developed framework can be implemented in the programmable communicating thermostats (PCTs) or the smart home energy manage systems (HEMSs) to enable residential inverter-driven ACs automatically respond to day-ahead dynamic electricity pricing.

关键词： Inverter-driven air conditioners Demand response Dynamic electricity pricing Genetic algorithm mixed integer nonlinear programming

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