Actual Plant Data Research Articles

Dynamic decarburisation model of AOD process: Based upon the FactSage-Macro Programme

Improving the quality of stainless-steel hinges significantly on precisely managing decarburisation within the argon-oxygen decarburisation (AOD) process. This study introduces a dynamic model capable of accurately predicting the composition and weight of steel and slag. To thoroughly investigate the refining processes, a systematic analysis was conducted, integrating three essential adiabatic zones within the AOD converter: the continuous stirred-tank reactor, slag bath and plug flow reactor, supported by circulation and recirculation streams. FactSageTM software and its macro facility were utilised to identify the different phases present in steel and slag. This dynamic model not only forecasts critical process parameters like temperature, composition and volume of multiple phases but also enables the prediction of the final chromium content for different initial carbon inputs, specifically at 2%, 1.5% and 1 wt.%. By comparing the model's predictions with actual plant data, it was observed that the transient compositions of steel and slag exhibited consistency, confirming the model's validity and reliability in simulating AOD refining processes.

Techno‐economic and carbon footprint analyses of steam Rankine cycle

AbstractSteam Rankine cycle (SRC), which is mainly utilised in power generation sector, faces external irreversibility in its daily operation causing inefficiency in the system. To address this issue, reheat Rankine cycle (RHRC) and regenerative Rankine cycle (RRC) have been widely studied and implemented in power plants to improve thermal efficiency and reduce external irreversibility of Rankine cycle. This study investigates the implementation of different RRC configurations in a combined heat and power plant, including RRC with modified thermal deaerator, RRC with open feed water heater (OFWH) and closed feed water heater (CFWH). A base case simulation model was first constructed using commercial simulation software Aspen HYSYS for the basic SRC system based on actual plant data. Various scenarios were then evaluated for their profitability and sustainability through techno‐economic analysis (TEA) and carbon footprint analysis (CFA). From both analyses, the scenario of RRC with CFWH showed the greatest long‐term potential, generating the highest annual profit of $ 771 691 and carbon footprint reduction of 14.63%, while RRC with modified thermal deaerator showed the greatest potential in the short run with the highest return of investment (ROI) of 201.51% and shortest payback period (PBP) of 0.50 year.

Investigation of an integrated liquid air energy storage system with closed Brayton cycle and solar power: A multi-objective optimization and comprehensive analysis

Energy storage has garnered global attention as a promising solution to the intermittent nature of renewable energy sources. For large-scale (>100 MW) energy storage technology, there are only three types: Pumped Hydroelectric energy storage (PHES), Compressed air energy storage (CAES) and Liquid air energy storage (LAES). The limitation of PHES is that several natural geological features are needed. The traditional CAES needs a combustion chamber, which will result in environmental problems. LAES offers several advantages over traditional CAES systems, including higher energy density, scalability, flexibility in site selection, lower environmental impact, cost-effectiveness, and compatibility with renewable energy sources. Under these conditions, LAES is more suitable than other systems. This investigation examined a novel zero-carbon system integrating LAES with CBC and solar power. The primary objective is to maximize the round-trip efficiency (RTE) of the system while simultaneously minimizing the total investment cost per unit of output power (ICPP). By systematically exploring the trade-offs between RTE and ICPP, the study seeks to identify the proposed system’s most efficient and economically viable configurations. Each subsystem was simulated using Aspen Plus® V12 and validated against actual plant data. The analysis indicates that the proposed optimal LAES-CBC system could achieve a remarkable increase in RTE up to 67.81 %, representing an increase of 11.18 % compared to the baseline. Additionally, the total ICPP could be reduced to 0.2739 $/kWh, marking a decrease of 5.96 %. The charging process of the LAES system emerges as a critical focal point due to its significant contributions to exergy destruction and equipment costs. This study provides valuable insights into enhancing and achieving maximum efficiency and cost-effectiveness. These insights are essential for accelerating the transition towards a carbon–neutral energy system, highlighting the importance of research in advancing sustainable energy technologies.

Implementation of dynamic start-up test experimental data as a main part of the nuclear code validation procedure: Developed RELAP5 model for VVER-1000

The main purposes of start-up tests in nuclear power plants (NPPs) are to ensure safe and reliable operation, verify system functionality, comply with regulatory requirements, optimize performance, and establish a foundation for ongoing plant operation and maintenance. However, the start-up tests of NPPs also could be used as a main part of the nuclear code validation procedure for several reasons including: realistic simulation, comprehensive evaluation, detection of code limitations, validation of safety margins and confidence in code predictions. The main purpose of the current study is to define and assess the validation procedure based on actual start-up test data. In this regard, the developed RELAP5 model has been validated against the actual data of VVER-1000 plant during a dynamic start-up test. The results of this full-scale validation show a good agreement between the developed RELAP5 model results and actual plant data. Finally, by defining a step by step validation procedure, it has been recommended to use the start-up phase test data as a more robust validation process which allow for full-scale validation of the nuclear code by comparing its predictions with actual plant measurements and also other advantages which have been demonstrated in the current study.

Steady State Simulation and Analysis of Crude Distillation Unit at Baiji Refinery

Abstract In this research work, the main distillation column of Salahadin 1 refinery in Baiji Refineries is simulated and analyzed using Aspen HYSYS. Kirkuk, Basrah and Ajeel crudes are mixed together (with unknown mixing ratio) to create the feed stream of this unit. The determination of the blending ratio of these streams beside performance analysis of the system using actual plant data to increase the operating capacity are the main objectives of this research work. So, this work is very helpful to the refinery because it can be applicable to manage these blending ratios to obtain desired product quality. Also, this work enables the CDU unit at the refinery to work at full capacity. The validation results show that the system is perfectly simulated with Aspen HYSYS and the mixing ratio of (Ajeel, Basrah, and Kirkuk) crudes are (0.102,0.5, and 0.398) respectively; Furthermore, this study suggested that, it is important to replace the transfer line pipe diameter between the furnace and the distillation column according to the design specifications which enables the operators to rise the feed volumetric flowrate into full capacity.

Event diagnosis method for a nuclear power plant using meta-learning

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI–based algorithm to be used in actual plants.

Comparison between Calculated, Simulated and Actual Plant Data of Multiple Effect Evaporators to Concentrate Sugarcane Juice

Multiple effect evaporators systems operating at steady state can be described by a series of nonlinear algebraic equations that include total and solute mass balances, energy balances, heat transfer rate equations, and the composition and temperature dependence of related thermodynamic properties such as vapor pressures and enthalpies. The main objective is to compare between the manual calculations, simulation and real data collected from Asalaya plant for validation the best design of multiple effect evaporators system in forward feed used in Asalaya Sugar Factory. In this design a short tube vertical evaporator, Calandria type has been used. The models include overall equations of materials balance, equations of energy balance and equations of heat transfer rate for measurement area for all the effects. These equations were solved using manual calculations methods and MATLAB coded program. The results of the present investigations have been compared with the data obtained from the Asalaya Sugar Factory with five evaporators. The results showed that the concentration of sugar cane juice at the final stage is equal to 60 Brix for the manual calculations, simulation and actual plant data with zero error. The temperature of solution in the pre- effect evaporator was found to be about (114.94, 114.94, 115)ºC, with a relative error equal to 0.05% and the steam temperature in the pre effect was found about (120)ºC for the manual calculations, simulation and actual plant data, with zero error. The area and number of tubes in the (pre, second, third and fourth) effect, are in a good agreement, with the standard value (Er=3.26%). The above results show great similarity with the real behavior of evaporator unit. This can be used as a reliable tool\for the simulation of different operating conditions.

Aspen plus simulation of an inline calciner for white cement production with a fuel mix of petcoke and producer gas

The white cement industry is facing the challenge of alternative fuel utilization replacing conventional fuel due to the impact of alternative fuel ash on the whiteness of clinker. On the other hand, municipal solid waste (MSW) disposal is a huge waste management problem worldwide. The present article focuses on utilizing MSW-based refuse derived fuel (RDF) as an alternative fuel in white cement. The ash-free producer gas derived via RDF gasification is proposed to overcome the challenge of direct RDF utilization. Aspen plus-based producer gas co-processing model for a calciner has been developed, considering 100% petcoke firing as the baseline scenario. The co-processing of producer gas further augmented the model to achieve a 15–20% thermal substitution rate (TSR). The developed model is validated using the actual plant data. The model results at 15% TSR predicted that the calciner outlet temperature will get reduced by 19 °C, with a 5.3% rise in calciner exit gas volume, which is manageable. CO2 mitigation potential at 15% TSR is estimated to be 11.33% of the baseline scenario. The TSR contribution of producer gas sensible heat at 593 °C is 2.7%, whereas the petcoke enters the system at 60 °C with negligible sensible heat.

Dynamic and Steady-State Simulation Study for the Stabilization of Natural Gas Condensate and CO2 Removal through Heating and Pressure Reduction

Stabilization of condensate is a highly energy-consuming process compared to other oil and gas processes. There is a need to reduce this energy consumption. Therefore, the present work aims to simulate the stabilization unit in terms of available energy and on-spec stabilized condensate products. Natural gas condensate liquids (NGL) need to be stabilized by eliminating lighter hydrocarbon gases and acid gases before being sent to the refinery. Stabilized NGL has the vapor pressure determined as a Reid vapor pressure of 7 psia, showing that light components did not evolve as a separate gas phase. Stabilization and CO2 removal was performed through the distillation method by heating and pressure reduction using steady state and dynamic simulation through Aspen HYSYS. Different process alterations around the exchanger and column have been studied based on the utilities available for the stabilization and CO2 removal process. Sensitivity studies, including the impact of CO2 concentration, the temperature at the inlet of the stabilizer flash separator, and the dynamic simulation for the PID controller, have been performed to analyze the impact on the process parameters, such as Reid vapor pressure (RVP) and CO2 of the rundown air cooler and heat duties of the exchangers. Actual plant data have been used for the validation of process simulation values for the accuracy of the condensate stabilization unit model. Based on the scenarios analyzed, it can be concluded that the nitrogen stripping method achieved 7 ppmv CO2 and 7 psia RVP in the condensate from the cooler outlet, while a variation of 29 bpd was observed for the stabilized condensate flowrate throughout all scenarios with data validation showing 0.24% discrepancy between Aspen Hysys data and actual plant data.

Simulation and optimisation of a medium scale reverse osmosis brackish water desalination system under variable feed quality: Energy saving and maintenance opportunity

In this work, we considered model-based simulation and optimisation of a medium scale brackish water desalination process. The mathematical model is validated using actual multistage RO plant data of Al- Hashemite University (Jordan). Using the validated model, the sensitivity of different operating parameters such as pump pressure, brackish water flow rate and seasonal water temperature (covering the whole year) on the performance indicators such as productivity, product salinity and specific energy consumption of the process is conducted. For a given feed flow rate and pump pressure, winter season produces less freshwater that in summer in line with the assumption that winter water demand is less than that in summer.With the soaring energy prices globally, any opportunity for the reduction of energy is not only desirable from the economic point of view but is an absolute necessity to meet the net zero carbon emission pledge by many nations, as globally most desalination plants use fossil fuel as the main source of energy. Therefore, the second part of this paper attempts to minimise the specific energy consumption of the RO system using model-based optimisation technique. The study resulted not only 19 % reduction in specific energy but also 4.46 % increase in productivity in a particular season of the year. For fixed product demand, this opens the opportunity for scheduling cleaning and maintenance of the RO process without having to consider full system shutdown.

Integration of a parabolic trough solar collector with an energy-intensive multi-effect evaporator: A move towards industrial decarbonization

The current study analyzes the potential of an environmentally benign integration of a parabolic trough solar collector (PTSC) with a heat-intensive multi-effect evaporator (MEE) used in pulp and paper industry. Actual plant data was used to simulate the MEE and estimate the steam economy (SE) and steam consumption (SC), which are used to judge the performance of PTSC-MEE system. Four distinct alternatives for the integration are investigated with Self-adaptive Differential Evolution (SaDE) utilized for optimization. Initially, a normal PTSC with four different model configurations of MEE, basic or with energy reduction schemes (ERSs) are tested and found that SE is slightly increased for each. Next, a geometrically optimized/combinatorial optimized PTSC with basic MEE is examined, a rise of ∼3.4% in SE is noted for both, with current integration outperforming one out of three ERSs. Subsequently, geometrical plus combinatorial optimized PTSC with basic MEE is inspected, increase in SE reached 7.59% and two of the three ERSs outperformed. Economic analysis is conducted to estimate the steam saving with different PTSC-MEE systems and corresponding payback periods. The possibility of decarbonization with the various integration possibilities is also explored by evaluating the fuel savings and related CO2 savings.

Towards retrofitting based multi-criteria analysis of an industrial gas sweetening process: Further insights of CO2 emissions

Natural gas processing is currently facing economic and environmental challenges due to abrupt changes in oil prices and the development of an alternate source of energy. Therefore it is essential to optimize the processing unit to make it profitable and environmentally friendly. Sustainable optimization of an industrial natural gas treatment plant is carried out using the NSGA-II algorithm for the methyl diethanol amine (MDEA) process to optimize CO2 removal along with payback period and damage index. This multi-objective optimization study includes seven decision variables such as temperature and pressure of feed gas, feed flow rate, temperature and pressure of regenerator feed, lean amine temperature, and MDEA concentration. Three separate two-objective optimization study problems are developed and applied for retrofitted case and base case studies. Two different ProMax models are developed and validated the model by using actual plant data. All the retrofitted cases and base cases are solved and the Pareto optimal fronts are obtained. Trade-offs between different objectives are illustrated for all the problems. The lean vapor compression process can facilitate maximum H2S removal of 99.75% and maximum CO2 removal of 98.52% simultaneously by maintaining a DI value of 476. TOPSIS method is used to rank and find the best optimal solution. Optimization study for uncertain CO2 concentration (4%mole) in the feed gas is also analyzed and compared with normal feed conditions. The machine learning approach is used to obtain the predictions of selected objective functions for all the problem cases using the decision tree method.

Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning.

Dry-Low Emission (DLE) technology significantly reduces the emissions from the gas turbine process by implementing the principle of lean pre-mixed combustion. The pre-mix ensures low nitrogen oxides (NOx) and carbon monoxide (CO) production by operating at a particular range using a tight control strategy. However, sudden disturbances and improper load planning may lead to frequent tripping due to frequency deviation and combustion instability. Therefore, this paper proposed a semi-supervised technique to predict the suitable operating range as a tripping prevention strategy and a guide for efficient load planning. The prediction technique is developed by hybridizing Extreme Gradient Boosting and K-Means algorithm using actual plant data. Based on the result, the proposed model can predict the combustion temperature, nitrogen oxides, and carbon monoxide concentration with an accuracy represented by R squared value of 0.9999, 0.9309, and 0.7109, which outperforms other algorithms such as decision tree, linear regression, support vector machine, and multilayer perceptron. Further, the model can identify DLE gas turbine operation regions and determine the optimum range the turbine can safely operate while maintaining lower emission production. The typical DLE gas turbine's operating range can operate safely is found at 744.68 °C -829.64 °C. The proposed technique can be used as a preventive maintenance strategy in many applications involving tight operating range control in mitigating tripping issues. Furthermore, the findings significantly contribute to power generation fields for better control strategies to ensure the reliable operation of DLE gas turbines.

A thermodynamic-based mixed-integer linear model of post-combustion carbon capture for reliable use in energy system optimisation

Assessing the role of carbon capture in energy systems dominated by non-dispatchable renewable energy sources requires a reliable and accurate model. However, carbon capture models used in complex systems optimisation are often very simplified. Therefore, we developed a mixed-integer linear model of post-combustion carbon capture starting from rigorous thermodynamic modelling in Aspen Plus. The final model decides the size and the operation of the capture process and returns the cost and energy requirements as a function of the CO2 concentration and the flow rate of the treated flue gas. Validation against actual plant data (Petra Nova) showed excellent accuracy with a deviation in total CO2 captured of just 2%. By applying the model to an exemplary case study, we show that it allows for co-optimising renewables deployment and carbon capture design and operation for a gas turbine, thus opening opportunities to explore new system designs of practical added value.

Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes

Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey-Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment's historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method.

Industrial practice and CFD investigation of a multi-chamber fluidized bed reactor for MnO2 ore reduction

The multi-chamber fluidized bed reactor (MCFBR) is beneficial to the higher solids conversion rate, but the complex configuration needs special methodology for the rational design and smooth operation. In this study, an actual plant data of MCFBR for the reduction of MnO2 ore were analyzed, which indicated that the recovery rate of Mn was confirmed to be kept above 95% normally. To investigate the multi-chamber fluidized bed hydrodynamics and reaction characteristics, the 3D computational fluid dynamics (CFD) numeration coupled with structure-based transfer model and multi-chamber calculation method was established. The effect of mesoscale structure on the prediction of multi-chamber reaction process was evaluated as well. The simulation results showed that the model was able to calculate the gas–solid reaction behavior more accurately than the traditional model without taking mesoscale structure into account. The computed tanks-in-series number of 2.0 is less than the actual chamber number of 4.0 for the wide gap between the vertical baffle and the side wall. The quantitative effects of partition configuration, fluidized structure, and operating condition on the solids conversion rate were estimated as well.

Analiza rada solarne elektrane u NTP Čačak – System Advisor Model (SAM) modelovanje i poređenje sa stvarnim rezultatima

In the 21st century, the use of renewable resources has been propagated by the specter of climate change and its associated impact. Renewable energy technologies have been deployed at a high rate and show a significant potential to become a major player in the electricity supply in next decade. PV systems have experienced a significant growth in their rate of deployment as an alternative to conventional power sources. This study presents the modeling and simulation of a 25 kWp solar power plant in Čačak, Serbia. The power plant is situated on the roof of the Science and Technology Park Čačak. The model was developed using System Advisor Model (SAM) software. SAM was used to analyze the technical performances of based on local weather conditions and all the system components of installed photovoltaic system. The model was validated by comparing its predictions with the actual plant data. The proposed predictive model can be used to characterize power plant performance as well as analyze and evaluate the system's efficiency.

A new way of working: Intelligent remote engineering via hybrid first principle modelling

The digital transformation era in the oil and gas industry is changing the way we operate our assets. A new way of working introduces automation of processes and a wider application of digital solutions. These improve remote engineering capabilities to maximise usage and applications of process advanced analytics to allow faster identification and troubleshooting of equipment and process issues. The foundation of remote diagnostic engineering is the hybrid of plant data and first principle simulation model. One of the main features of the tool is harnessing a process simulation model’s strength in producing first-principle verified data and analysis, as example, a predictive composition properties model and virtual flow analysers. This soft sensor approach fills in the gaps of actual plant data availability and quality. Another main feature is the enablement of performance monitoring and diagnostics which includes equipment such as compressors, pumps, and heat exchangers. Adaptation of the new ways of working concepts will capture the intended outcomes namely reduced HSSE risks, improved profitability, higher capital efficiency and pandemic proofing. In the future, a wider application of the intelligent remote engineering tool is recommended to introduce prescriptive analysis, enriching insights for improved operational excellence.

PV module life prediction based on coupled failure model

Due to environmental stresses, photovoltaic (PV) modules operating outdoors may trigger multiple power degradation patterns. In this paper, four types degradation patterns are studied, among which humidity cycling is proposed for the first time. To verify the necessity of the proposed humidity cycle degradation, the sensitivity of the environment on the output power of PV modules is analyzed using orthogonal tests, and the results show that the power degradation of PV modules by humidity cycle is second only to the damp heat degradation. In addition, a method to model the coupling relationship between each degradation patterns based on weight values is proposed to accurately quantify the influence of environment on the output power of PV modules, and the accuracy of the model is verified by actual power plant data.

AI-guided optimization of manufacturing protocols for AHSS coils

ABSTRACT Manufacturing protocols or processing parameters in a coiling mill affect multiple desired properties of advanced high-strength steel (AHSS) coils. These properties include yield strength (YS), ultimate tensile strength (UTS), elongation percent, hardness, etc. In this work, attempts were made to maximize YS, UTS, and elongation percent for AHSS coils while determining the operating parameters that can be helpful in achieving those properties. Additionally, operating parameters were also determined for a few specific grades of AHSS steel with respect to desired properties of interest. Actual plant data from a coiling mill was analyzed through a set of statistical and artificial intelligence (AI) based algorithms. Predictive models were developed through k-Nearest Neighbor (k-NN) algorithm. Optimization of multiple properties was performed through a non-dominated sorting genetic algorithm (NSGA2). The concept of parallel coordinate chart (PCC) was used for visualization as well as identifying operational parameter that can be helpful in achieving a desired property. The research methods and findings presented in this article are of industrial significance and can be applied to other manufacturing processes.