Simulation Model Of Process Research Articles

Simulation-Based Design for Recycling of Car Electronic Modules as a Function of Disassembly Strategies

Modules (or parts) of a car are a complex functional material combination used to deliver a specified task for a car. Recovering all materials, energy, etc., into high-grade materials at their end of life (EoL) is impossible. This is dictated by the second law of thermodynamics (2LT) and thence economics. Thus, recyclability cannot be conducted with simplistic mass-based approaches void of thermodynamic considerations. We apply, in this paper, a process simulation model to estimate the true recyclability of various SEAT (Volkswagen Group) car parts within the EU H2020 TREASURE project. This simulation model is developed with 190 reactors and over 310 feed components with over 1000 reaction species in the 880 streams of the flowsheet. The uniqueness of the work in this paper is to apply the full material declaration (FMD) and bill of materials (BOM) of all 310 materials in the parts as a feed to the process simulation model to show the parts’ true recyclability. We classified all parts into categories, i.e., copper-rich, steel-rich and plastic-rich, to maximally recover metals at the desired material quality, as well as energy. Recyclability is understood to create high-grade products that can be applied with the same functional quality in these parts. In addition, disassembly strategies and related possible redesign show how much recyclability can be improved. Process simulation permits the creation of alloys, phases, materials, etc., at a desired quality. The strength of the simulation permits any feed from any End-of-Life part to be analyzed, as long as the FMD and BOM are available. This is analogous to any mineral and metallurgical engineering process simulation for which the full mineralogy must be available to analyze and/or design flowsheets. This paper delivers a wealth of data for various parts as well as the ultimate recovery of materials, elements, and energy. The results show clearly that there is no one single recycling rate for elements, materials, and alloys. It is in fact a function of the complexity and material combinations within the parts. The fact that we use a thermochemical-based process simulator with full compositional detail for the considered parts means full energy balances as well as exergy dissipation can be evaluated. This means that we can also evaluate which parts, due complex mixtures of plastics, are best processed for energy recovery or are best for material and metal recovery, with thermochemistry, reactor technology and integrated flowsheets being the basis.

Research on multi-objective energy-saving optimization of propylene distillation column

The energy-saving optimization problem of the propylene distillation column was investigated. A PRO/II process simulation model was established based on the actual operating data of the unit and a sensitivity analysis of the operating parameters was conducted. On this basis, a multi-objective optimization model for the unit was established using regression analysis. The penalty function mechanism and dynamic search factor were incorporated into the MOGWO algorithm to solve the optimization model. The optimization results demonstrate that by optimizing multiple sets of operating parameters, the propylene distillation column can increase its production while reducing its energy consumption, achieving energy-saving optimization for the unit. This method provides a new theoretical basis for the energy-saving optimization design of propylene distillation units and other distillation processes.

Optimizing emergency department efficiency: a comparative analysis of process mining and simulation models to mitigate overcrowding and waiting times

ObjectiveOvercrowding and extended waiting times in emergency departments are a pervasive issue, leading to patient dissatisfaction. This study aims to compare the efficacy of two process mining and simulation models in identifying bottlenecks and optimizing patient flow in the emergency department of Al-Zahra Hospital in Isfahan. The ultimate goal is to reduce patient waiting times and alleviate population density, ultimately enhancing the overall patient experience.MethodsThis study employed a descriptive, applied, cross-sectional, and retrospective design. The study population consisted of 39,264 individuals referred to Al-Zahra Hospital, with a sample size of at least 1,275 participants, selected using systematic random sampling at a confidence level of 99%. Data were collected through a questionnaire and the Hospital Information System (HIS). Statistical analysis was conducted using Excel software, with a focus on time-averaged data. Two methods of simulation and process mining were utilized to analyze the data. First, the model was run 1000 times using ARENA software, with simulation techniques. In the second step, the emergency process model was discovered using process mining techniques through Access software, and statistical analysis was performed on the event log. The relationships between the data were identified, and the discovered model was analyzed using the Fuzzy Miner algorithm and Disco tool. Finally, the results of the two models were compared, and proposed scenarios to reduce patient waiting times were examined using simulation techniques.ResultsThe analysis of the current emergency process at Al-Zahra Hospital revealed that the major bottlenecks in the process are related to waiting times, inefficient implementation of doctor’s orders, delays in recording patient test results, and congestion at the discharge station. Notably, the process mining exercise corroborated the findings from the simulation, providing a comprehensive understanding of the inefficiencies in the emergency process. Next, 34 potential solutions were proposed to reduce waiting times and alleviate these bottlenecks. These solutions were simulated using Arena software, allowing for a comprehensive evaluation of their effectiveness. The results were then compared to identify the most promising strategies for improving the emergency process.ConclusionIn conclusion, the results of this research demonstrate the effectiveness of using simulation techniques and process mining in making informed, data-driven decisions that align with available resources and conditions. By leveraging these tools, unnecessary waste and additional expenses can be significantly reduced. The comparative analysis of the 34 proposed scenarios revealed that two solutions stood out as the most effective in improving the emergency process. Scenario 19, which involves dedicating two personnel to jointly referring patients to the ward, and scenario 34, which creates a dedicated discharge hall, have the potential to create a more favorable situation.

Применение имитационного моделирования в процессах постройки судна

This article provides an example of the application of the method of simulation modeling of ship construction processes in AnyLogic software. The process is briefly described and the logistics of building a barge produced at shipyards is characterized, and a map of the production process is prepared. In conclusion, three possible modernization options were proposed, which consist in technological, organizational improvements and a variant combining both of these solutions. AnyLogic software was used for a comparative analysis of the proposed options in relation to the process in the basic version. This method allows you to obtain accurate data for making informed decisions without the need for physical tests. The use of simulation modeling is a key tool for optimizing shipbuilding processes and further development of the industry.

Simulation Model of One-Parameter Selective Assembly of Two Elements With Multivariate Set-Making

Analytical and simulation methods are used to model assembly processes and technological systems in order to determine and optimize their performance and functioning parameters. The first one, describes an object or process using mathematical relationships indirectly. Simulation modelling is one of the modern methods of production system experimental research, allowing to take the presence of various components in the system, nonlinear characteristics, non-determinism and dynamism of ongoing processes into account. This work is devoted to the simulation model development of the technological process of multivariate one-parameter selective assembly of two elements. Multivariate set-making is defined by generalized rules with a set of weights which elements are set the distribution of elements between sets of different types. Selectively assembled elements have parameters determined by the manufacturing stage, which are independent random variables with known probabilistic characteristics. It is assumed that the input-output relationship is linear with given coefficients, the set-making equations linking the numbers of selective groups and assembly sets are known. A simulation model of the assembly process was built in the GPSS World, which, in contrast to the known one, operates with multivariate set-making rules for the case of assembly of two elements. The model allows to determine the quantitative indicators of the assembly process, in particular, the probabilities of assembly set formation, work-in-progress and preliminary rejects. As an example, the process of multivariate set-making of two parts forming a precision engineering product is considered. The data of parts distributions of both types by selective groups before assembly are given, the technological process indicators are calculated. Based on the modelling results the advantages and disadvantages of the multivariate set-making process are outlined.

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

Economic feasibility of the biorefinery processing bamboo residues with biphasic phenoxyethanol-acid pretreatment technology: Techno-economic analysis

This study conducted a techno-economic analysis to assess the economic feasibility of a biorefinery converting bamboo residues to bioproducts via phenoxyethanol-acid pretreatment. The analysis is integrated with a rigorous process simulation model with the support of experiment data. The bioproducts include ethanol or polylactic acid (PLA), with lignin-based wood adhesive as the byproduct. The minimum selling price (MSP) is $515-$819 per t ethanol and $1,512-$1,842 per t PLA when biphasic treatment is deployed, compared to the MSP of $2,318-$2,721 per t ethanol and $2,859-$3,211 per t PLA for no biphasic pretreatment. Adopting a ratio of 1:1 phenoxyethanol: acid solution (1%) in pretreatment decreases the final product yield but leads to lower MSP than 4:1 phenoxyethanol: acid solution (1%) due to lower phenoxyethanol consumption. A sensitivity analysis identifies the key drivers of reducing MSP, including increasing economic gain from the lignin-based wood adhesive, lowering the phenoxyethanol cost, and expanding plant capacity.

A new system using refuse-derived fuel as a feedstock for hydrogen and power co-generation: A techno-environmental assessment

Refuse-derived fuel (RDF) offers a promising opportunity for Canada to proceed with its transition towards a circular economy. This study focuses on the development of a pseudo-steady-state process simulation model to integrate RDF processing and utilization technologies with the existing plants. Canada’s RDF is chosen as a potential feedstock and is coupled with the two cycles, namely Sulfure Iodine (S-I) thermochemical cycle for hydrogen production and dual pressure loop power (DPLP) cycle for electricity generation. A comprehensive process modeling and simulation is undertaken in the Aspen Plus software package. An integrated environmental and thermodynamic assessment of the plant, including energy and exergy analyses, is conducted. Several sensitivity analyses are conducted to predict the ideal operational parameters for optimized efficiency. It is found that a feed of 1300 kg/h RDF generates 96.76 kg of clean hydrogen and 382.24 kW of clean power. The results show RDF-to-power and RDF-to-H2 energy efficiency of 22.19 % and 25.27 % respectively. Regarding the environmental performance, the results show that the proposed plant avoids 423.82 tons of CO2 emissions per year for clean hydrogen production and 1685.45 tons of CO2 emissions per year for clean power production. The overall performance of the developed system in terms of efficiencies is 35.91 % for energy and 42.46 % for exergy.

Process simulation and techno-economic analysis of CO2 capture by coupling calcium looping with concentrated solar power in coal-fired power plant

The calcium looping (CaL) cycle is an effective high-temperature CO2 capture technology, its primary energy consumption arises from the calcination process, which limits the development of CaL technology. Utilizing concentrated solar power (CSP) instead of combustion for the calcination process in the CaL cycle can significantly reduce the energy penalty associated with CO2 capture of coal-fired power plants. Currently, the application of coupling CaL with concentrated solar power (CaL-CSP) systems in coal-fired power plants is still in its infancy, and there is limited research on its thermal performance and techno-economic analysis. In this study, a process simulation model of a 660 MW supercritical coal-fired power plant is developed as a reference. Subsequently, four different heat transfer routes are integrated into the design of CaL-CSP systems. The results indicate that the coupled power plant achieves a CO2 capture rate of 90 % compared to the reference coal-fired power plant, and reduces annual CO2 emissions by 93 % to only 58.2 g CO2/kWh. The CO2 capture energy penalty is 0.24 MJ/kg CO2. A sensitivity analysis of power generation cost reveals that if the cost of solar concentrator components is halved, the power generation cost will decrease to just 25 % higher than the reference power plant. Given the rapid advancements in CSP technology, this reduction is likely to be realized soon. Moreover, with the increase in carbon taxes and carbon prices, the power generation cost is expected to further decrease.

Improving the accuracy of weighing systems in motion using dynamic neural networks

The paper is devoted to solving the problem of weighing vehicles in motion as part of modern information technologies and automated intelligent systems for managing urban resources and infrastructure. The aim of the work is to improve the accuracy of measurements in systems for weighing vehicles in motion in heavy traffic as part of intelligent urban infrastructure management systems, thereby contributing to the efficiency and sustainability of urban processes. The scientific novelty is the use of models in the form of neural networks with time delays to process data from weighing sensors. The application of this approach makes it possible to improve the accuracy of mass measurement in weighing systems in motion under conditions of intense traffic by taking into account the dynamic and nonlinear properties of the weighing process. The practical usefulness of the developed method lies in the development of new innovative weighing systems as part of modern information technologies and automated intelligent systems for managing urban resources and infrastructure. Testing the method on a simulation model of the WIM process demonstrated advantages in weighing accuracy compared to traditional methods using static and linear models. Experimental data showed a reduction in the root mean square error compared to the traditional method based on Kalman filters, which confirms the effectiveness of the proposed approach.

A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

AbstractThe occurrence of process‐induced deformations of composites laminates is challenging for assembly accuracy and may lead to a service life reduction of parts. However, it can be obviously mitigated through different optimization strategies on the basis of the accurate curing process simulation. In this study, a stacking sequence optimization strategy is proposed and applied to multi‐layer thick asymmetric laminates. The shapes of deformed laminate plates are experimentally investigated in virtue of the three‐dimensional coordinate measuring machine. The thermo‐chemical–mechanical behaviors of plates are first verified through the comparisons of model predicted and experimental process‐induced deformations. Then the nonlinear control formula achieved through the regression model is proposed for the direct relationship between stacking sequences and process‐induced deformations. Finally, the required solutions are generated by solving the control formula. With the comparisons between the average deformations before and after optimizations, it is found that the magnitudes of deformations are significantly reduced, especially when the unoptimizated deformations are large.Highlights The thermo‐chemical–mechanical behavior can be described in the curing process simulation model. The deformed shapes of multi‐layer thick asymmetric laminate plates are measured by a three‐dimensional coordinate measuring machine. The stacking sequence optimization strategy is implemented by introducing a nonlinear control formula.

Equilibrium modelling of steam gasification of PKS system and CO2 sorption using CaO: A digitalized parametric and techno-economic analysis

The conversion of palm oil waste into energy can complement the energy mix with renewable energy and bring economic benefits to the palm oil industry. A process simulation model for palm kernel shell (PKS) steam gasification for H2-enriched syngas with the integration of CO2 capturing using CaO has been investigated using Aspen Plus V10®. Techno-economic and energy analyses have also been conducted to identify energy-saving opportunities for commercialization. The effect of process variables, including reactor temperature (600–800 °C), Steam/PKS ratio (0.5–2 wt/wt), and CaO/PKS ratio (0–1.5 wt/wt), have been determined, with the predicted results compared to the reported experimental data. H2 concentration has been increased with 76–78 vol% with the temperature elevation from 650 to 750 °C. Additionally, a substantial increase in H2 content from 68 to 72vol% was observed when the steam flow rate was increased from 0.5 to 1.5. Conversely, the CO2 concentration decreased from 25 to 8vol% as the adsorbent ratio was raised from 0.5 to 1.5. The techno-economic analysis showed that the capital investment is $4.11 million, and the operating cost is $3.89 million per year, which is also very high due to the high raw material costs. In the case of energy, saving that 3.03 and 1.513 Gcal/hr can be saved in terms of utilities and gas cooling that economized the process which shows that utilization of these in heat exchange networks can significantly reduce costs, with up to 78 % savings in heat exchanger capital and 35 % in total energy consumption. The energy potential could be harnessed through the digitalization of the process.

A framework for measuring the quality of business process simulation models

Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate the impact of adding one or more resources on the cycle time of a process. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and from empirical observations, or automatically discovered from historical execution data. Regardless of its provenance, a key question when using a BPS model is how to assess its quality. In particular, in a setting where we are able to produce multiple alternative BPS models of the same process, this question becomes: How to determine which model is better, to what extent, and in what respect? In this context, this article studies the question of how to measure the quality of a BPS model with respect to its ability to accurately replicate the observed behavior of a process. Rather than pursuing a one-size-fits-all approach, the article recognizes that a process covers multiple perspectives. Accordingly, the article outlines a framework that can be instantiated in different ways to yield quality measures that tackle different process perspectives. The article defines a number of concrete quality measures and evaluates these measures with respect to their ability to discern the impact of controlled perturbations on a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the proposed measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify the sources of discrepancies.

Characterization of damage behavior and failure mechanisms of the bonded‐bolted hybrid three‐bolt joint in composite laminates

AbstractIn this paper, the mechanical properties, damage modes, and bolt load distribution of the bonded‐bolted hybrid three‐bolt joint were investigated by a combination of experiments and numerical simulations. Acoustic emission (AE) was used to monitor the tensile process of the connection in real time, and the K‐means clustering algorithm was used to realize the cluster analysis of the damage modes. A numerical simulation model of the tensile process of the joint was established using ABAQUS simulation software. The progressive damage evolution of composite laminates, and adhesive layer was characterized by using the VUMAT subroutine, the B‐K failure criterion, and cohesive element with zero thickness, respectively. The results show that four main damage modes occur in composite laminates during tension: matrix cracking, delamination, fiber pull‐out, and fiber breakage, with corresponding peak frequency ranges of (0–80 kHz), (80–210 kHz), (210–330 kHz), and (330–375 kHz), respectively. The adhesive layer between the laminates mainly exhibits peeling failure because of the secondary bending effect. In the load‐bearing process, the adhesive layer was loaded first and failed, and the bolt loading was delayed. Meanwhile, the results of bolt load distribution showed that the bolts and the adhesive layer at both ends carried greater loads.Highlights Damage modes and damage evolution of the bonded‐bolted hybrid joint in tension were analyzed by combining acoustic emission technology and numerical simulation. The simulation of delamination damage in composite laminates was carried out by inserting zero‐thickness cohesive elements into the laminates, and the delamination damage between the carbon fiber layup and the adjacent glass fiber layup was analyzed. The effect of the inclusion of the adhesive layer on the mechanical properties of the joint and the distribution of the bolt load was analyzed.

Applications of simulation modeling in mining project risk management: criteria, algorithm, evaluation

Project risk management in the mining industry is necessary to identify, analyze and reduce uncertainty. The engineering features of mining enterprises, by their nature, require improved risk management tools. This article proves the relevance of creating a simulation model of the production process to reduce uncertainty when making investment decisions. The purpose of the study is to develop an algorithm for deciding on the economic feasibility of creating a simulation experiment. At the same time, the features and patterns of the cases for which the simulation experiment was carried out were studied. Criteria for feasibility assessment of the model introduction based on a qualitative parameters became the central idea for algorithm. The relevance of the formulated algorithm was verified by creating a simulation model of a potassium salt deposit with subsequent optimization of the production process parameters. According to the results of the experiment, the damage from the occurrence of a risk situations was estimated as a decrease in conveyor productivity by 32.6%. The proposed methods made it possible to minimize this risk of stops in the conveyor network and assess the lack of income due to the risk occurrences.

Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR

During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations.

Addressing Launch and Deployment Uncertainties in UAVs with ESO-Based Attitude Control

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

Finite element method based modeling of cutting forces and cutting temperature in micro-milling LF21

The demands for aluminum alloy LF21 micro precision parts are increasing in the fields of aerospace, high-tech electronic products, and the other fields. Micro-milling is an effective technology for machining small LF21 precision parts. Cutting forces and temperature are crucial factors in micro-milling process, directly affect tool vibration, tool wear, and surface quality of the workpiece, and even result in large deformation of the tool and workpiece. Direct measurement of cutting forces during micro-milling requires high-precision and expensive instruments. Moreover, due to the small cutting area in micro-milling, it is challenging to achieve accurate measurements of cutting area temperature. Therefore, accurate prediction of cutting forces and temperature in micro-milling is urgent and challenging. Nowadays, there are few studies on prediction of cutting forces in micro-milling LF21. The study on prediction of temperature in micro-milling LF21 is still blank. To solve the above problems, this paper proposes a finite element method based on modeling for prediction of cutting forces and temperature in micro-milling LF21. ABAQUS software is adopted. First, the geometry models of the micro-milling tool and workpiece are established. Then, the assembly and mesh division of the established models are completed. Johnson-Cook constitutive model and Johnson-Cook damage criteria are used to describe the material constitutive relationship and chip separation criteria, respectively. The suitable tool-workpiece friction models are determined. Finally, the simulation of the micro-milling LF21 process is achieved. Experiments of micro-milling LF21 are conducted and the cutting forces are measured using the dynamometer. The validity of the built process simulation model and the correctness of the cutting force prediction results are verified by the comparison of experiment and simulation cutting forces. Then, the prediction of temperature is achieved based on the verified process simulation model of micro-milling LF21.

Thermodynamic Simulation Model of Copper Side-Blown Smelting Process

In this study, the thermodynamic simulation model and system of the copper side-blown smelting process were established using the chemical equilibrium constant method, based on the process reaction mechanism, multiphase equilibrium principle, and MetCal software platform (MetCal v7.81). Under typical production conditions, the composition of the product and the distribution behavior of impurity elements were simulated. The results indicate that the average relative error between the calculated mass fractions of major elements such as Cu, S, Fe, SiO2, CaO, MgO, and Al2O3 in copper matte and smelting slag, and the actual production values, is 4.25%. Additionally, the average relative error between the calculated distribution ratios of impurity elements such as Pb, Zn, As, Bi, Mo, Au, and Ag in copper matte and smelting slag, and the actual production data, is 6.74%. Therefore, this model and calculation system accurately reflects the actual production situation of the copper side-blown smelting process well and has potential to predict process output accurately while optimizing process parameters, effectively guiding production practice.

Recent progress in the operational flexibility of 1 MW circulating fluidized bed combustion

In this study, various experimental tests were carried out with a 1 MWth circulating fluidized bed pilot plant at the Technical University of Darmstadt to investigate the combustion and co-combustion behaviour of different fuels under load change conditions in air and oxyfuel mode. The combustion tests were carried out with low-calorific lignite and hard coal as reference cases for co-combustion. In the co-combustion experimental tests, straw pellets, and refuse-derived fuels (high and low calorific) were used as co-fuels for coal. In addition, a comprehensive dynamic 1.5-D process simulation model was established for a 1 MWth circulating fluidized bed pilot plant. The model created with the “APROS” software precisely reproduces both the gas/solids and the water/steam side of the 1 MWth pilot plant. The dynamic process simulation model was first validated at various steady-state operation points and, in a second step, at various dynamic load changes. The numerical results, such as the pressure drop and temperature profiles along the fluidized bed riser as well as the flue gas concentrations at the cyclone outlet, were compared with the measurements and showed agreement during the long-term tests of the combustion and co-combustion processes.