Optimal Operation Research Articles

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

Unlocking Co3O4–ZnO p-n heterojunction for superior acetone gas sensing detection

In this study, we synthesized pure ZnO and Co3O4–ZnO precursors with varied Co, Zn ratios via solvothermal method, and then the precursors were calcined at 400 ​°C for 2 ​h in a muffle furnace under air to obtain composites for acetone detection. The structure, morphology, elemental composition, microstructure and chemical state of these materials were systematically studied by various characterization techniques. Additionally, we also evaluated the gas sensing performance of the composites-based sensors, focusing on optimal operating temperature, baseline resistance, repeatability, stability, selectivity, response/recovery time, and resistance under varying relative humidity. The findings reveal that 3 ​% Co3O4–ZnO-based sensor exhibit the highest response value to 100 ​ppm acetone (74), showing an enhancement of approximately 9.3 times compared to the pure ZnO-based sensor (8). Furthermore, the 3 ​% Co3O4–ZnO-based sensor demonstrate the advantages of rapid response/recovery times (15 ​s/2 ​s), outstanding selectivity, and remarkable stability. The gas sensing mechanism of the composite material is also discussed in detail, which provides insights into the observed enhancement of gas sensing performance. It provides an idea for the follow-up study on gas sensing performance of acetone sensors.

Dynamic operation of distribution grids with the integration of photovoltaic systems and distribution static compensators considering network reconfiguration

The optimal operation of Distribution Networks (DNs) using network reconfiguration has become more critical in the modern power system due to the widespread use of Renewable Energy Sources (RESs) and the imbalance between load demand and energy provided by RESs. However, attaining the most efficient functioning while integrating RESs is a challenging endeavor due to the unpredictability of the electrical system and the complexities associated with network reconfiguration. Integrating Distribution Static Compensators (D-STATCOMs) with network reconfiguration is powerful for improving voltage deviation reducing overall costs, and minimizing active power losses, while successfully accommodating the fluctuating characteristics of renewable energy sources. To tackle these difficulties, we offer the Horned Lizard Optimization Algorithm (HLOA), a new approach for optimizing the operation of DNs. The efficacy of HLOA is showcased on the IEEE 33-bus DN, to minimize costs, voltage deviations, real power losses, and emissions in the presence of unpredictable factors like photovoltaic (PV) uncertainties, price changes, and load demand. The analysis encompasses three case studies: one focused on optimizing operation solely with PV integration, another using both PV and D-STATCOM integration, and a third incorporating PV, D-STATCOMs, and network reconfiguration. The results indicate that the combination of PV, D-STATCOMs, and network reconfiguration significantly decreases overall cost by 45.6 %, real power losses are reduced by 66.3 %, and voltage variations are improved by 71.04 %. Emissions are mitigated by 36.72 % compared to the base case.

Process Modeling and Optimization of Supercritical Carbon Dioxide-Enhanced Geothermal Systems in Poland

This paper presents a comprehensive analysis of supercritical carbon dioxide (sCO2)-enhanced geothermal systems (EGSs) in Poland, focusing on their energetic performance through process modeling and optimization. EGSs harness the potential of geothermal energy by utilizing supercritical carbon dioxide as the working fluid, offering promising avenues for sustainable power generation. This study investigates two distinct configurations of sCO2-EGS: one dedicated to power generation via a binary system with an organic Rankine cycle and the other for combined power and heat production through a direct sCO2 cycle. Through accurate process modeling and simulation, key parameters influencing system efficiency and performance are identified and optimized. The analysis integrates thermodynamic principles with geological and operational constraints specific to the Polish context. The results highlight the potential of sCO2-EGSs to contribute to the country’s energy transition, offering insights into the optimal design and operation of such systems for maximizing both power and thermal output while ensuring economic viability and environmental sustainability.

RLclean: An unsupervised integrated data cleaning framework based on deep reinforcement learning

Data cleaning, a prerequisite to subsequent data analysis, has always been the focus of data science research. Datasets with errors can severely detract from the quality of downstream analytical results. Unfortunately, despite the proliferation of various data cleaning methods, it remains a time-consuming problem and frequently entails considerable labor expenses. In reality, errors are often heterogeneous and require different solutions. As a result, stand-alone methods often inadequate for addressing dirty data with multiple types of errors, while studies have demonstrated that combining such methods always require human intervention and the result remains unsatisfactory.In this paper, we propose an unsupervised integrated data cleaning framework, namely RLclean. Based on deep reinforcement learning, RLclean takes advantages of multiple data cleaning techniques, enabling it to effectively clean multiple types of errors and achieve satisfactory results. Additionally, it eliminates the need for costly human involvement, as the cleaning strategy is learned by data-driven, which further allows the framework to self-adapt to diverse domains. RLclean mainly consists of two parts: (i) an integrated error detection model that unites multiple techniques to detect different types of errors from multiple views; and (ii) an integrated data repair model that learns the optimal repair operations and repairs dirty data in an unsupervised manner. Extensive experiments on benchmark datasets have demonstrated the superiority of RLclean over state-of-the-art methods.

Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation

Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation

Efficient Preparation and Optimization of Activated Carbon Monoliths from Resorcinol-Formaldehyde Resins for CO2 Capture

Activated carbon monoliths with developed porosity, high surface area and excellent adsorption properties were successfully prepared from resorcinol-formaldehyde resins using a physical activation method. The primary objective of this study was to examine the impact of key parameters, namely hexamethylenetetramine content (0.08–0.2 g), pyrolysis heating rate (5–20 °C/min) and activation time (1–7 h), on the final characteristics of the activated carbon in order to identify the optimal operating conditions to achieve the desired properties. All the cured resin samples were pyrolyzed at 900 °C under a nitrogen atmosphere, while the activation process took place in the presence of CO2. The evaluation of the activated carbon materials was based on the CO2 adsorption capacity and BET surface area, micropore area and total pore volume, which were employed as the criteria for selecting the optimal activated carbon. The synthesized porous carbon monoliths exhibited good properties: high BET surface area (900 m2/g), high CO2 adsorption capacity (5.33 mmol/g at 0 °C and 1 bar, 3.8 mmol/g at 25 °C and 1 bar) and good CO2 selectivity for CO2/N2 and CO2/CH4 mixtures. These results were obtained with a pyrolysis heating rate of 5 °C/min and a 3 h activation period.

Cost-efficient microgeneration renewable energy provision dimensioning for sustainable 5G heterogeneous network

The deployment of mobile networks has imposed an urgent requirement for the pursuit of low-carbon communication infrastructures. The increasing energy consumption of mobile networks has brought about challenges of techno-economic and environmental sustainability. Renewable energy-enabled mobile networks have received a lot of attention due to their capability to evade greenhouse gas emissions and easy availability. Microgeneration-based renewable energy provision is a feasible and effective solution for 5G networks. Dimensioning of microgeneration renewable energy power supply is an essential issue to make the system operate for a long period cost-effectively with a minimum amount of grid energy consumption. For effective deployment of microgeneration renewable energy system, it is essential to provision it with adequate PV panel capacity and storage devices. This work attempts to identify the cost-effective, energy-efficient, and emissions-aware sizing of PV panels and storage for 5G HetNet. An energy-saving strategy based on an optimal policy of advanced sleep modes and traffic-aware load offloading is developed and the interaction of the energy-saving strategy on the system dimensioning is explicitly examined. The proposed solution aims to ensure the communication quality of service whilst keeping the optimal cost-effective deployment and network operation. The system performance in terms of grid energy consumption, empty storage probability, emission performance, and total cost of the system is extensively assessed through experiments for a range of operational scenarios. The numerical results demonstrated that the proposed sustainable energy system dimensioning and operation integrated with an energy-saving strategy is energy-efficient and cost-effective.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

APPLICATION OF THE METHOD OF GROUP ARGUMENTS ACCOUNTING FOR BUILDING MODELS OF SHORT-TERM FORECASTING OF GAS CONSUMPTION BY THE POPULATION

Problem statement. One of the most important tasks in optimizing the operation modes of urban gas distribution systems is to develop a short-term strategy for such modes. Processing of planned and statistical indicators of gas consumption modes is one of the key methods for ensuring the optimal functioning and normal operation of gas supply systems. The most promising modeling method for solving forecasting problems is the method of self-organization of models implemented as the method of group arguments accounting (MGАA). Short-term forecasting of gas consumption is the main source of information for decision-making in the process of planning consumption regimes and operational dispatch control. The MGАA method, also known as the Ivakhnenko method, is a powerful tool for obtaining highly accurate forecasting models and is gaining importance for forecasting in energy resource management and planning their use. The solution to such a problem is relevant, as it makes it possible to analyze the volume of natural gas consumption by the population for the future period. The purpose of the article is to build mathematical models for forecasting gas consumption using the method of group arguments accounting based on input data. Conclusion. To select the best models the following parameters are recommended: tomorrow's gas consumption as a function (main criterion) and arguments (additional criterion) - gas consumption to date, air temperature to date, yesterday's temperature and temperatures for the past 3, 5, 7 days, wind speed and day of the week. The resulting mathematical models of natural gas consumption by the population can be used to analyze the volume of natural gas consumption. The method allows us to find a single optimal mathematical model that best solves the problem.

Assessment of sensor-based automatic smart watering unit for paddy nurseries under Indian perspective

A sensor-based automatic watering unit was developed to irrigate the paddy seedling trays. The watering unit comprised six flat spray nozzles with a direct current (DC) water pump and flow sensor, and these were kept at the top of the conveyor frame, which was actuated by a DC water pump with a solenoid valve to drop the water in trays. The actuation of the DC solenoid valve was controlled by a limit switch fitted to the conveyor frame, which sensed the moving trays on the conveyor. The performance of the developed unit was evaluated, and process optimization was performed using the Inscribed Central Composite Design in Response Surface Methodology. The effect of main operating parameters- chain conveyor speed (0.123, 0.196, and 0.27 m/s), spray operating pressure (1.5, 3.0, and 4.5 kg/cm2), and spray height (10,30 and 50 cm) on the performance of the sensor-based automatic watering unit was studied in terms of the amount of water spray requirement and coefficient of water spray uniformity in trays. The responses of the unit at its optimal parameter operation (spray operating pressure 4.5 kg/cm2, spray height 10 cm, and chain conveyor speed 0.14 m/s) were found to be 1.31 L/tray as the amount of water spray requirement and 92.41 % as the coefficient of water spray uniformity of the unit. Hence, it can be an efficient substitute with significantly less power consumption 41 W with a higher working capacity of 780 trays/h for watering into the paddy tray nursery. The developed sensor-based automatic smart watering unit saves irrigation water per square area of 68.90 %, 76.13 %, and 82.31 % compared to existing drip, sprinkler, and flood irrigation methods in mat-type paddy nurseries. The developed sensor-based automatic smart watering unit saves the watering cost INR 11,567.16 (140.02 USD) per hectare, reducing cost by 63.7 % compared to manual watering in the mat-type nursery preparation. Thus, an automatic smart watering unit in a paddy tray nursery offers consistent and uniform watering from an Indian perspective, promoting uniform germination, crop growth, and establishment. It conserves water by ensuring the right amount of water is applied at the right time, avoiding overwatering or under-watering.

Evaluation of PWV products derived from satellite-based and radiosonde retrievals for the eastern anatolia observatory (DAG)

In this study, we have presented the results of the precipitable water vapor (PWV) for the Eastern Anatolia Observatory (in Turkish: Doğu Anadolu Gözlemevi, the acronym is DAG) site in Erzurum, Türkiye. The DAG has Türkiye’s largest and the first near infrared (NIR) telescope with a mirror diameter of 4 meters at the altitude of 3170 m. The DAG telescope is going to take the first light in the end of summer 2024. This study is focused on the examining of the precipitable water vapor data for the NIR observations at the DAG. In this context, the NWC SAF Total Precipitable Water (TPW) data obtained by both the satellite based and the radiosonde balloon validated with six radiosonde stations were examined by temporal, vertical and horizontal analyses for the DAG site between June 2019 to December 2020. The results obtained from these analyzes indicate that the mean and median TPW values at the DAG site were approximately 7 mm and the minimum and maximum values were 0.59 mm and 24.12 mm, respectively. The monthly median TPW values at the DAG site varied between approximately 3-10 mm, with a decreasing trend from June to January and an increase in the first seven months of 2020. These results also indicate that the TPW data obtained by its 15 minutes temporal resolution, aligns closely with the radiosonde measurements. Furthermore, the values of PWV at both lower and upper levels of the atmosphere are minimal while the values increase slightly in the middle layer of the atmosphere. As a result, the effective monitoring of the PWV in a site would result in the generation of higher quality astronomical IR observations and be important in terms of the optimum operating cost for an observatory.

Building a system of diagnostics of problem areas in the management of business processes of the organization

Aim. To develop and formulate an algorithm that allows timely diagnosing and eliminating problems in business processes.Objectives. To consider and systematize the main signs of problems in business processes; to determine the main sources, causes of losses and inefficiency; to formulate an algorithm that allows to assess the effectiveness of the process; to test the proposed algorithm for assessing the effectiveness of business processes on the example of a specific organization.Methods. The authors used the methods of descriptive literature review and statistical data analysis. The research includes the study and analysis of scientific articles, reports of scientific conferences and other publications related to the subject of the article. The analysis of statistical data is based on the performance of the organization, in particular the operator of urban mobility services. All data used for calculations are of a conditional nature.Results. In the process of research the works of modern authors related to the analysis of efficiency and diagnostics of problems in business processes are considered and generalized. In the mentioned works some theoretical aspects of the topic are widely and in detail studied, which are then systematized by the authors of the present study. As a result, an algorithm has been formed that allows in practice to monitor the efficiency of business processes of the organization, as well as to diagnose possible problems in a timely manner. When testing this approach in practice, in the context of the analysis of business processes in the organization, the following results were obtained: a number of critical problems in business processes that have a negative impact on the result of activity were identified; a plan to improve their efficiency was developed and implemented.Conclusions. The process approach is one of the most successful tools to improve the efficiency of the organization. Optimal operation of all key and essential business processes ultimately affects the company’s result. The operating conditions of an organization are constantly changing under the influence of a dynamic business environment. One of the most important elements of the efficiency management system for business processes, and consequently for the business as a whole, is a properly formed set of indicative indicators reflecting the progress of each process. The choice of indicators should be based primarily on the strategic goals of the organization, cascaded down to the level of individual processes. The study confirmed the hypothesis that the risks of efficiency reduction can be identified in advance and in a timely manner with the help of a correctly constructed system of simple control indicators. For these purposes, taking into account the concept of lean production and continuous improvement, as well as on the basis of the classification of losses set out in it, a questionnaire for express analysis of business process efficiency has been developed.

One-pot chemo- and photo-enzymatic linear cascade processes.

The combination of chemo- and photocatalyses with biocatalysis, which couples the flexible reactivity of the photo- and chemocatalysts with the highly selective and environmentally friendly nature of enzymes in one-pot linear cascades, represents a powerful tool in organic synthesis. However, the combination of photo-, chemo- and biocatalysts in one-pot is challenging because the optimal operating conditions of the involved catalyst types may be rather different, and the different stabilities of catalysts and their mutual deactivation are additional problems often encountered in one-pot cascade processes. This review explores a large number of transformations and approaches adopted for combining enzymes and chemo- and photocatalytic processes in a successful way to achieve valuable chemicals and valorisation of biomass. Moreover, the strategies for solving incompatibility issues in chemo-enzymatic reactions are analysed, introducing recent examples of the application of non-conventional solvents, enzyme-metal hybrid catalysts, and spatial compartmentalization strategies to implement chemo-enzymatic cascade processes.

Robust optimization of cascaded MSMPR crystallization unit using unsupervised machine learning

AbstractThe use of mixed suspension mixed product removal (MSMPR) system in the pharmaceutical industry to produce active pharmaceutical ingredients is well known. In industrial settings, the MSMPR system is subject to lot of process uncertainty which, if ignored, might result in poor product quality. In this work, the process uncertainty involved in MSMPR is targeted during the process optimization stage to find robust optimal operating conditions. The temperature and the residence time inside each cascaded MSMPR unit, altogether six, are considered as uncertain parameters. A sampled set of uncertain data points for such six different uncertain parameters are clustered using a novel support vector clustering (SVC) based algorithm. The uniqueness of this algorithm lies in its ability to fine‐tune the hyper‐parameters of SVC while intelligently clustering the uncertain data points into optimal number of clusters. Such identified clusters are helpful to generate more samples from the intended regions rather than generating them randomly to avoid proposing conservative solutions. Both best‐case and worst‐case scenarios for robust oOptimization (RO) are considered with , and samples. As the model has to be evaluated for a large number of samples and the MSMPR models are time‐consuming to evaluate, a surrogate model of the MSMPR process is developed to perform optimization under uncertainty. Performance metrics are used to quantitatively establish the superiority of the SVC based RO over the box‐sampling based RO.

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Improving the quality of Arabica coffee (Coffea arabica L.) through innovative optimization of roto-aerated drying

This study focused on enhancing the drying process of coffee by introducing an innovative device called as roto-aerated dryer. Traditional methods often result in prolonged drying times and undesirable degradation, prompting the exploration of alternative techniques. The research involves evaluating drying performance, establishing correlations between physical-chemical attributes and coffee quality, and determining optimal operating conditions to achieve a balance between drying efficiency and product quality. The Central Composite Design was employed, varying air velocity (vf), air temperature (Tf), and solid feed rate (Ws). The results demonstrated increased drying efficiency while preserving key quality parameters, including acidity, water activity, sugars, and organic acids. The desirability function identified the optimal levels of the operational variables as vf = 2.7 m·s−1, Tf = 81.8 °C, and Ws = 40 g·min−1. This study introduced an innovative approach to enhance the drying efficiency of coffee while ensuring exceptional product quality, providing valuable insights for the coffee industry. Industrial relevanceCoffee, one of the most important global commodities, undergoes ongoing enhancements in post-harvest processing to achieve exceptional quality, particularly in specialty coffee. Conventional methods face challenges related to processing time and the potential compromise of quality. These challenges are effectively addressed through the adoption of innovative technologies such as the use of the roto-aerated dryer, as proposed in this work.

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Design and assessment of tractor-driven chopping tilling and mixing machine for in-situ management of paddy straw

In many Indian regions, paddy wheat is the main crop rotation and facing the problem of straw incorporation for seed bed preparation in short period. The handling of straw in combine harvested paddy fields is a significant issue in the paddy wheat rotation. In order to solve this issue, efforts were carried out to cut paddy straw into small pieces by the newly proposed counter-rotating blades, followed by the simultaneous incorporation of a rotary tiller into the soil. Therefore, a tractor operated chopping cum tilling mixing machine was developed. In the study, three different blades (straw management system (SMS) Serrated, cutter bar and SMS plain) were tested in the terms of torque and required to chop the straw. SMS serrated blade was best suitable for the chopping mechanism as it required minimum cutting torque for the straw bunches. The developed chopping cum tilling mixing machine was tested at three different levels of forward speed (1.77, 2.3, and 3 km h−1), moisture content at three levels (35 ± 3.4, 24 ± 2.2 and 17 ± 2.6%) with fix rotary tiller rotational speed of 810 rev min−1. Optimum operating condition of the machine was obtained at a forward speed of 1.9 km h−1 and a moisture of 24%. At these optimized values, the mixing index (96%), mean weight diameter (7.9 mm), bulk density (1230 g cc−1) and fuel consumption (3 l h−1) were 96%, 7.9 mm, 1230 g cc−1 and 3.0 l h−1 respectively. The developed machine performs three operations i.e., chopping, tilling, and mixing in single pass for effective in-situ straw management.

FloorDiffusion: Diffusion model-based conditional floorplan image generation method using parameter-efficient fine-tuning and image inpainting

The conditional generation of high-quality floorplan images using deep-learning methods is challenging because the generated floorplans are required to match specific conditions, such as floorplan silhouettes and spatial layouts. Recently, diffusion models have emerged as alternatives of conditional generative adversarial networks in image generation, offering higher image quality, pairing-free training datasets, and adaptability to various image domains via parameter fine-tuning of pretrained diffusion models. However, diffusion models are rarely used for floorplan generation because when fine-tuning them on image domains that were not learned in pretraining, such as floorplans, the quality of the generated images is poor and tuning takes a long time. This phenomenon arises from the so-called catastrophic forgetting problem, where traditional fine-tuning methods that update all parameters easily destroy the knowledge of pretrained diffusion models. To address this problem, we propose FloorDiffusion, a diffusion model-based conditional floorplan generation method. In this method, only a few key parameters of the pre-trained diffusion model are fine-tuned, which allows adaptation to the floorplan domain while retaining its useful knowledge. Then, the fine-tuned diffusion model performs conditional floorplan generation by inpainting the unfinished regions of the input conditional image. Comparative experiments with existing methods demonstrate that our method can produce more architecturally realistic floorplan images with up to 72 % image quality improvement. It can also generate various floorplan images for a single input condition image. Finally, ablation studies show that all components of the proposed method are essential for optimal operation.