Steady-state Process Model Research Articles

Thermodynamic performance of a cryogenic energy storage system based on natural gas liquefaction

Cryogenic energy storage (CES) is a viable method for grid-scale electrical energy storage. Considering the high energy density and mature application of liquefied natural gas (LNG), we proposed an LNG cryogenic energy storage (LNGES) system. A steady-state process model of the LNGES system was established using Aspen HYSYS. The effects of the natural gas composition and key operating parameters such as the charging pressure, discharging pressure, throttling temperature, and liquid storage pressure on the system performance were investigated. A multi-parameter genetic algorithm model built using the MATLAB software was used to optimize the LNGES system to optimize the round-trip efficiency (RTE). Then, an exergy analysis of the optimal configuration was conducted. The results suggested that the LNGES system could achieve optimal RTE and exergy efficiency values of 60.14% and 71.64%, respectively. Exergy destruction mainly occurred during the compression, throttling, expansion, and heat exchange. The proposed LNGES system could be a promising candidate for the large-scale application of CES technology in power grids and gas networks.

Combined extreme learning machine and max pressure algorithms for traffic signal control

Nowadays, rush-hour traffic congestion problems persist in most major cities around the world, resulting in increased pollution, noise, and stress for citizens. Therefore, an optimal traffic light strategy is needed. For this purpose, several models have been proposed. However, these models often overlook the non-stationarity of traffic, which occurs due to changing traffic conditions over time. Additionally, these models are steady-state process models, leading to a decrease in their predictive power over time. To address these issues, this paper proposes the combination of two algorithms: a passive Extreme Learning Machine with periodic mini-batch learning (PB-ELM) for predicting traffic flow and the Max Pressure control algorithm (MPA) for signal control. In the first step, the passive periodic Extreme Learning Machine (PB-ELM) adjusts quickly and regularly based on new data, overcoming traffic non-stationarity and improving long-term performance. In the second step, the MPA is preferred for signal control due to its simplicity and speed. The PB-ELM-MPA model is a combination of predictive algorithms that takes the current road network conditions as input and predicts the flow of vehicles at intersections. The model utilizes learned characteristics of the source and destination roads to estimate the number of vehicles in each movement. The PB-ELM outputs serve as the starting point for the max-pressure algorithm, which reduces congestion by considering only the vehicles on road segments closest to the intersection and selecting the highest pressure at each time interval. The proposed PB-ELM-MPA model is evaluated on an isolated intersection simulated with the SUMO micro-simulator, demonstrating a significant improvement in avoiding traffic jams. The total staying time of all vehicles present at the intersection is reduced by 65% compared to the fixed configuration of traffic lights. Additionally, CO2 emissions and fuel consumption are reduced by approximately 34% compared to the classic MPA and Deep Q-Network approaches.

Plant and system-level performance of combined heat and power plants equipped with different carbon capture technologies

Installing carbon capture and storage (BECCS) capability at existing biomass-fired combined heat and power (bio-CHP) plants with substantial emissions of biogenic CO2 could achieve significant quantities of the negative CO2 emissions required to meet climate targets. However, it is unclear which CO2 capture technology is optimal for extensive BECCS deployment in bio-CHP plants operating in district heating (DH) systems. This is in part due to inconsistent views regarding the perceived value of high-exergy energy carriers at the plant level and the extended energy system to which it belongs. This work evaluates how a bio-CHP plant in a DH system performs when equipped with CO2 capture systems with inherently different exergy requirements per unit of CO2 captured from the flue gases. The analysis is based upon steady-state process models of the steam cycle of an existing biomass-fired CHP plant as well as two chemical absorption-based CO2 capture technologies that use hot potassium carbonate (HPC) and amine-based (monoethanolamine or MEA) solvents. The models were developed to quantify the plant energy and exergy performances, both at the plant and system levels. In addition, heat recovery from the CO2 capture and conditioning units was considered, as well as the possibility of integrating large-scale heat pumps into the plant or using domestic heat pumps within the local DH system. The results show that the HPC process has more recoverable excess heat (∼0.99 MJ/kgCO2,captured) than the MEA process (0.58 MJ/kgCO2,captured) at temperature levels suitable for district heating, which is consistent with values reported in previous similar comparative studies. However, using energy performance within the plant boundary as a figure of merit is biased in favor of the HPC process. Considering heat and power, the energy efficiency of the bio-CHP plant fitted with HPC and MEA are estimated to be 90% and 76%, respectively. Whereas considering exergy performance within the plant boundary, the analysis emphasizes the significant advantage the amine-based capture process has over the HPC process. Higher exergy efficiency for the CHP plant with the MEA capture process (∼35%) compared to the plant with the HPC process (∼26%) implies a relatively superior ability of the plant to adapt its product output, i.e., heat and power production, and negative-CO2 emissions. Furthermore, advanced amine solvents allow the BECCS plant to capture well beyond 90% of its total CO2 emissions with relatively low increased specific heat demand.

Modeling and experimental investigation for development of Combined Electrolysis and Catalytic Exchange process for hydrogen isotope separation

A process based on the Combined Electrolysis and Catalytic Exchange (CECE) has been developed for separation of hydrogen isotopes. Indigenously developed Platinum supported on Carbon Aerogel based hydrophobic catalyst was used for hydrogen-water isotope exchange reaction combined with hydrophilic packing in a catalytic exchange column. The exchange column has been studied in detail for various operating conditions in this study. The intrinsic kinetics for the catalytic reaction was determined through spinning basket reactor experiments, and activation energy was estimated to be 29.7 kJ/mol. The overall mass transfer co-efficient in a mixture of hydrophilic packing and hydrophobic catalyst packed bed was determined experimentally in a pilot scale counter current trickle bed reactor to be in the range of 0.15–0.25 s-1 for the proposed range of operating conditions. Individual mass transfer resistances for vapour-liquid mass transfer, external gas mass transfer and gas-solid mass transfer were estimated using widely accepted empirical correlations. Under benchmark operating conditions, the Damkohler number for the exchange column was found to be between 0.10 and 0.16, indicating a significant contribution of surface reaction to overall mass transfer resistance. The vapour liquid mass transfer was found to be fast compared to the gas-vapour reaction. Therefore, increasing hydrophobic catalyst proportion relative to hydrophilic packing was found to increase the overall mass transfer. A steady state process model has been developed to predict the performance of overall exchange process using the experimentally determined parameters. Validation of the process model was carried out using the experimental data generated on the pilot scale trickle bed reactor. Optimum operating conditions of temperature, hydrophobic catalyst to hydrophilic packing ratio, gas and liquid superficial velocities were determined.

Comparative study on the globally optimal performance of cryogenic energy storage systems with different working media

Cryogenic energy storage (CES) has garnered attention as a large-scale electric energy storage technology for the storage and regulation of intermittent renewable electric energy in power networks. Nitrogen and argon can be found in the air, whereas methane is the primary component of natural gas, an important clean energy resource. Most research on CES focuses on liquid air energy storage (LAES), with its typical round-trip efficiency (RTE) being approximately 50% (theoretical). This study aims to explore the feasibility of using different gases as working media in CES systems, and consequently, to achieve a high system efficiency by constructing four steady-state process models for the CES systems with air, nitrogen, argon, and methane as working media using Aspen HYSYS. A combined single-parameter analysis and multi-parameter global optimization method was used for system optimization. Further, a group of key independent variables were analysed carefully to determine their reasonable ranges to achieve the ideal system performance, that is, RTE and liquefaction ratio through a single-parameter analysis. Consequently, a multi-parameter genetic algorithm was adopted to globally optimize the CES systems with different working media, and the energy and exergy analyses were conducted for the CES systems under their optimal conditions. The results indicated the high cycle efficiency of methane and a low irreversible loss in the liquefaction cycle. Moreover, the Joule-Thomson valve inlet temperature and charging and discharging pressures considerably affected the system performance. However, exergy loss in the CES system occurred primarily in the compressor, turbine, and liquefaction processes. The maximum optimal RTE of 55.84% was achieved in the liquid methane energy storage (LMES) system. Therefore, the LMES system is expected to exhibit potential for application in the CES technology to realize the integration of natural gas pipelines with renewable power grids on a large scale. Moreover, the results of study have important theoretical significance for the innovation of the CES technology.

Passive learning to address nonstationarity in virtual flow metering applications

Steady-state process models are common in virtual flow meter applications due to low computational complexity, and low model development and maintenance cost. Nevertheless, the prediction performance of steady-state models typically degrades with time due to the inherent nonstationarity of the underlying process being modeled. Few studies have investigated how learning methods can be applied to sustain the prediction accuracy of steady-state virtual flow meters. This paper explores passive learning, where the model is frequently calibrated to new data, as a way to address nonstationarity and improve long-term performance. An advantage with passive learning is that it is compatible with models used in the industry. Two passive learning methods, periodic batch learning and online learning, are applied with varying calibration frequency to train virtual flow meters. Six different model types, ranging from data-driven to first-principles, are trained on historical production data from 10 petroleum wells. The results are two-fold: first, in the presence of frequently arriving measurements, frequent model updating sustains an excellent prediction performance over time; second, in the presence of intermittent and infrequently arriving measurements, frequent updating in addition to the utilization of expert knowledge is essential to increase the performance accuracy. The investigation may be of interest to experts developing soft-sensors for nonstationary processes, such as virtual flow meters.

Optimization of a cryogenic liquid air energy storage system and its optimal thermodynamic performance

For grid-scale intermittent electricity storage, liquid air energy storage (LAES) is considered to be one of the most promising technologies for storing renewable energy. In this study, a steady-state process model was developed for an LAES, by combining a Linde liquefaction process and an open Rankine power cycle. To investigate the system performance and achieve global optimization, a single-factor analysis approach and multifactor genetic algorithm (GA) optimization model were built using MATLAB software. The effects of the charging pressure, storage pressure, discharging pressure, and isentropic efficiency of the compressor/turbine on the LAES performance parameters, such as the inlet temperature of the Joule–Thomson valve, round-trip efficiency (RTE), liquefaction ratio (LR), and power consumptions in the compressor, cryo-pump, and turbine, were investigated. Subsequently, the optimal conditions were obtained using the GA optimization method to achieve the optimal performance of the LAES. The results showed that the charging pressure, discharging pressure, and isentropic efficiencies of the compressor and turbine had significant effects on the RTE; an increase in the discharging pressure resulted in an improved expansion power output; the GA optimization could achieve RTE, LR, the system energy storage and recovery exergy efficiencies of 53.33%, 86.96%, 81.00%, and 78.16%; the power consumption in the compressor of GA optimization was 10.02% maximum saving. The proposed optimization method can be used to further explore the global optimization of cryogenic energy storage systems, such as different-layout LAES systems and different cryogenic liquefaction media energy storage systems.

Assessment of the solvent-electromagnetic heating-based bitumen extraction technology through techno-economic modelling

Bitumen’s high viscosity makes its extraction a challenging operation. The steam-assisted gravity drainage is a widely used extraction method for bitumen in a deep reservoir, however, it is greenhouse gas (GHG) emission-intensive because of high fossil fuel consumption. There is a lot of interest in bitumen extraction technologies with a low GHG emissions footprint. Solvent-electromagnetic Heating (SEH) is an emerging technology that can lower GHG emissions in oil sands extraction. SEH uses an electromagnetic device and solvent to preheat and mobilize emulsion from the reservoir. SEH eliminates the dependence on condensation heat and therefore the strict requirement of solvent purity. However, few studies have explored its techno-economic feasibility. Unlike studies found in the literature, this study presents a detailed techno-economic assessment, where plant size, operating and economic parameters are evaluated to measure their impacts on the economics of SEH technology. A rigorous steady-state process model of the SEH was developed for a capacity of 25,000 bbl/day of bitumen. The supply cost of dilbit, a mixture of bitumen and diluent, was evaluated. The estimated supply cost is C$56.80/bbl. Taking uncertainty into consideration, this cost ranges from C$55.20/bbl to C$64.40/bbl with median and mean values of C$60.20/bbl and C$59.90/bbl, respectively. Maintenance cost, solvent cost, blending cost, and transportation cost were major contributors to the supply cost of the SEH dilbit. The developed scale factor for SEH is 0.85, which suggests that a large plant capacity will reduce the dilbit cost. The SEH process is cost-competitive with conventional oil sands extraction methods and can be considered as a potential replacement for new greenfield projects. The findings of this study provide insight into the economic performance of SEH, thus can be used for decision-making.

Set-point optimization for plant-wide control of a sugarcane mill under process and market prices disturbances: Energy and economic perspectives

Optimization and control strategies are necessary to keep factory operations profitable while minimizing energy usage. Like many industries, the need for the sugarcane industry to improve its energy and economic efficiency is often interrupted by random variations in process inputs and market prices. Therefore, this study seeks to present a plant-wide steady-state optimal operation policy. The strategy uses a steady-state process model and a surrogate optimization algorithm to find optimal set-points that maximize factory revenue in the presence of disturbances. The controlled variables are considered as the decision variables and, after solving the optimization problem, they are provided as setpoints for the closed-loop control systems. To assign an economic value to optimal plant-wide control, the factory net revenue is defined as the objective function. A Monte Carlo method is used to randomly sample from a distribution of process and economic disturbances and to simulate the effect thereof on the energy efficiency and economics of a sugar mill. With set-point optimization, an 8.5% increase in surplus bagasse production was economically optimal despite a 0.43% reduction in sugar yield, improving the net revenue by 2.4%. Therefore, the optimizer managed to determine the optimal points at which excess energy expenditure no longer justifies the revenue gain from sugar. Considering, the global shift towards bio-based products, the increase in surplus bagasse makes it possible for a relatively large bio-refinery that uses bagasse as feedstock to be annexed to a set-point optimized sugarcane mill.

Heat transfer modelling of semi-suspension biomass fired industrial watertube boiler at full- and part-load using CFD

In the current work, a computational fluid dynamics model is presented of a semi-suspension fired bagasse boiler which pays special attention to resolving the heat transfer to the furnace walls, superheater tubes and evaporator tube bank. The boiler CFD model includes custom-developed sub-models programmed in the C language which captures the fuel particle grate interaction, cylindrical fuel particle drag effects, superheater steam side heat transfer and evaporator heat exchange using a porous media approach. The model is applied to simulate the case study boiler for 100% and 65% load cases. The CFD model results are verified using a steady-state process model which utilizes well-known empirical heat transfer correlations. The CFD model results are also validated using plant measurements. The results of the CFD model show that it can accurately predict the outlet steam temperatures of the superheaters with relative errors of between 0.89 and 1.6% compared to the site data. The model is also capable of accurately predicting the evaporator outlet gas temperatures with a relative error of approximately 1.4%. Once the model is verified and validated it is used to investigate heat flux and metal temperature distributions in the various heat exchangers. The results show that at part load conditions the combustion occurs much closer to the furnace rear wall due to deeper spreading of the fuel particles, which results in the front wall having a relatively low heat absorption rate compared to the side and rear walls.

Simulation and Application of NOX Emission Characteristics in the Vertical Flue of 6 m Coke Oven

In view of the formation process of nitrogen oxide in the vertical flue of coke oven, a three-dimensional steady-state mathematical model of heat transfer, mass transfer and combustion process was established, and the numerical method was used to solve the problem. The effects of exhaust gas circulation ratio, preheating temperature of blast furnace gas and air and the blending ratio of coke oven gas on the NOX generation characteristics were studied. The above parameters were further optimized to the model that was applied to 6-m coke oven of Ansteel. The results show that the numerical simulation results are in good agreement with theoretical combustion temperature and the NOX emission data of 6-m coke oven in Ansteel. Through the optimization scheme, the preheating temperature of gas and air is the most significant one, followed by ratio of exhaust gas circulation and coke oven gas blending. The optimized model is applied to 6 m coke oven of Ansteel, and the average content of NOX in the exhaust gas is reduced from 364 to 228 mg/m3, with good effect.

Valorizing Waste Derived Fuels for Climate Change Mitigation: Techno-Economic Comparison of CCS and CCU Approaches based on the Carbonate Looping Process

It is common understanding that a reduction of anthropogenic CO2 emissions is undispensible. Furthermore, already emitted CO2 has to be removed from the atmosphere. To meet these objectives, novel processes must be developed and applied. Besides the utilization of renewable energy sources and a reduction of energy intensity, the application of carbon capture and storage/utilization (CCS/U) technologies is required. In a waste-to-energy (WtE) plant, the carbon contained in the waste is largely converted to CO2 and released to the environment. Thereby, heat and power are produced as valuable by-products of waste treatment. The carbon is partly of biogenic origin (e.g. wood and food waste) and of fossil origin (e.g. plastics derived from mineral oil). By separation, storage or utilization of the CO2 contained in the WtE plant flue gas, municipal solid waste (MSW) is utilized as a resource for clean energy and net negative CO2 emissions. The carbonate looping (CaL) process represents an efficient option for post-combustion CO2 capture from WtE plants. In this process, the heat for sorbent regeneration can be supplied by solid recovered fuels (SRF), which are sourced of pretreated MSW. Based on the results of experimental investigations in an SRF-fueled CaL pilot plant at 1 MWth scale, this study focuses on the techno-economic assessment and comparison of two valorization pathways for captured CO2, namely i) long term storage and ii) utilization to produce methanol. Key performance indicators (KPI) of the process chain, such as production costs of methanol and levelized costs of electricity (LCOE) are calculated and compared. Based on a steady-state process model for the CaL process, investment costs are determined according to a bottom-up approach for each component. Operational costs are derived from relevant material and product streams. The economic analysis takes various investment and operation scenarios into account to assess techno-economic characteristics in light of the future energy market. Factors such as negative emissions credits, low electricity cost, increased gate fees or plant size are considered.

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

Estimation of the optimum propane content for the Spheripol Polypropylene process

A Real Time Optimization (RTO) algorithm is proposed to optimize the reactors’ propane content for the Spheripol Polypropylene process. This algorithm improves the present approach of arbitrarily selecting “optimum” propane values, based on monthly/yearly average balances. The optimum propane content depends on factors such as the costs of offgas and catalysts, the propane content in the feedstock and the catalyst mileage. The steady state process model is developed and tested over plant data to prove its predictive capabilities. Results from the off-line, as well as the in-line implementation of the algorithm are presented that demonstrate its applicability. Estimates on the amount of the expected savings for a medium size Polypropylene plant are also presented.

Improving NGL recovery process with dividing-wall column for offshore applications

Fractionation process within natural gas processing facility is an essential step that allows to obtain liquid light hydrocarbons that are often used as feedstocks in petrochemical industries. In this article, a process scenario that includes a heat integrated distillation column is proposed for natural gas liquids (NGL) recovery and assessed through multiscale simulations. Aspen HYSYS software was used to develop steady state process models whose results were used to estimate capital and utility costs. It was noticed that the proposed process has the highest capital cost and lower operating cost which reduces remarkably the total annual cost, this process can also achieve 60 % ethane and 97 % propane recoveries with no additional of external refrigeration. Heat integration, low TAC, separation efficiencies for ethane and propane components, self-refrigeration and more importantly a possibility of operating the process in ethane recovery and ethane rejection modes are the main highlights of the proposed NGL recovery process.

Multi-objective optimization of high-sulfur natural gas purification plant

There exists large space to save energy of high-sulfur natural gas purification process. The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas. A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax. Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution. To solve the problem that the process model does not converge in some conditions, back-propagation (BP) neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model. The uniform design method and the table U21 (107) have been applied to design the experiment points for training and testing BP model. High prediction accuracy can be achieved by using the BP model. Non-dominated sorting genetic algorithm-II has been developed to optimize the two objectives, and 100 Pareto optimal solutions have been obtained. Three optimal points have been selected and evaluated further. The results demonstrate that the total comprehensive energy consumption is reduced by 13.4% and the production rate of purified gas is improved by 0.2% under the optimized operating conditions.

Parametric analysis of a steady state equilibrium-based biomass gasification model for syngas and biochar production and heat generation

In this study, a comprehensive steady-state model of downdraft biomass gasification process was developed using Aspen Plus simulation software. All four major gasification zones: drying, pyrolysis, oxidation, and reduction were modeled separately. Oxidation and Reduction zones were modeled using the Gibbs free energy minimization method with restricting chemical equilibrium approach. The simulated model was compared with the results of a wood-fed downdraft and sawdust fed gasifier reported in the literature. A parametric analysis was performed to analyze the effect of the gasifying agent, gasification temperature (500–1000 °C), equivalence ratio (0.1–0.5), and moisture content (0–25%) on the performance of gasifier. Performance evaluation of gasifier for syngas production and heat generation was performed by determining the low heating value of syngas, cold gas efficiency and thermal efficiency. The results indicated that the Equivalence ratio (ER) is the most important factor in gasification. Changes in the ER shows a very significant variation in syngas composition. Moreover, the increase in cold gas and thermal efficiencies was also very prominent until 700 °C after which it becomes constant. Biochar production increases with a decrease in moisture content and equivalence ratio.

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO2 Capture Using Pilot-Plant Data

This paper is focused on the development and validation of a dynamic model for a monoethanolamine-based CO2 capture unit. Starting with a rigorous steady-state process model, the dynamic model is d...

Process modelling and analysis of intensified CO2 capture using monoethanolamine (MEA) in rotating packed bed absorber

Rotating packed bed (RPB) absorber using monoethanolamine (MEA) as the solvent to capture CO2 is modelled at steady state condition in this study according to the first principles in gPROMS®. The effect of eight different kinetic reaction models and five enhancement factors is examined based on the newly developed model. Selection of kinetic model has a significant effect on the carbon capture level (CCL) but the effect of enhancement factor relation is not important. The steady state process model is validated against the experimental data and showed good agreement. The average absolute relative deviation for 12 case-runs is 3.5%. In addition, process analysis is performed to evaluate the effect of four factors namely rotor speed, MEA concentration in lean MEA solution, lean MEA solution temperature and lean MEA solution flow rate on CCL. Finally, the orthogonal array design (OAD) method is applied to analyse the simultaneous effect of the above-mentioned factors in the CCL and motor power of RPB absorber by considering 25 scenarios. The result of using OAD revealed that rotor speed has the most important effect on CCL, and after that lean MEA solution flow rate has the second importance. In addition, the OAD method is used to find the proper combination of four factors that resulted in about 90% CCL with low motor power.

Compact steam bottoming cycles: Model validation with plant data and evaluation of control strategies for fast load changes

Power plants installed on offshore oil and gas installations need to be operated in a flexible manner in order to accommodate the variability in heat and power demands. The present paper describes steady-state process model validation based on data from an actual offshore oil and gas installation, dynamic model validation, and evaluation of control strategies for fast load changes. The offshore process configuration consisted of two gas turbines with a once-through heat recovery steam generator located downstream of each gas turbine. One steam turbine received the combined steam mass flow from the two steam generators. The validation data, focusing on the steam bottoming cycle, consisted of one year of operation. Subsequently, a dynamic process model based on a simplified process layout was developed in the open physical modeling language Modelica and validated with reference steady-state and transient software data. The results from the evaluation of control strategies showed the benefits in utilizing feedforward control for the operation of the heat recovery steam generator under fast load changes, and the effectiveness of attemperation to avoid excessive excursions of live steam temperature during transients.