Operation Optimization Research Articles

Adopting Systematic Review in Conceptual Digital Maturity Modelling: A Focus on Facilities Management Sector

In the rapidly evolving digital landscape organizations must undergo digital transformation (DT) to maintain competitiveness. However, the facilities management (FM) sector lacks systematic guidance in its DT efforts. The absence of a specific digital maturity model (DMM) for the FM sector hinders its ability to effectively navigate and benchmark its DT progress. This paper aims to address this gap by proposing a conceptual maturity model tailored to the FM sector, providing a roadmap for continuous improvement in DT. The research adopts a systematic literature review (SLR) approach, utilizing content analysis to select and analyse 21 relevant papers. The SLR identifies crucial dimensions for achieving DT maturity, encompassing strategy and leadership organization, technology, process, client (end-user), people and data management. Building on these findings, a conceptual Facilities Management Digital Maturity Model (FM-DMM) is proposed. Theoretically, this study advances the understanding of DT in FM by integrating dynamic capability theory, highlighting the necessity of specific capabilities for DT maturity. Practically, the FM-DMM offers a structured framework for FM organizations to benchmark their DT progress, aiding in strategic decision-making and operational optimization. This model provides targeted guidance for successfully navigating the complex terrain of DT in the FM sector.

Joint Planning and Operation Optimization of Renewable Energy Systems Considering Bi-Directional Electric-Thermal Storage and Conversion

Renewable energy systems have emerged as a crucial research area due to the escalating demand for sustainable energy solutions. With the advancement of renewable energy, the electric-thermal coupling within multi-energy systems has become more intricate. Bi-directional electric-thermal storage and conversion technologies have emerged as a potential solution to address the challenges associated with efficient energy utilization. This paper focuses on the joint planning and operation optimization of renewable energy systems considering bi-directional electric-thermal storage and conversion. The integrated framework for renewable energy systems incorporating a bi-directional electric-thermal storage and conversion unit is designed, and the joint planning and operation optimization method is proposed. Case studies are conducted based on typical annual energy demand and solar radiation characteristics in Beijing, China. Numerical results show that the proposed method can effectively handle the coupling and bi-directional conversion characteristics of electrical and thermal energy, achieving energy cost savings while fulfilling the energy demands of the system. The proposed system has a capital expenditure of USD 261,251.4 and an operating expenditure of USD 177,007.1, which shows a total cost reduction of 12.28% compared to the lithium-ion battery system, providing better economic performance while further enhancing the flexibility of energy utilization. These research findings contribute to the development of more efficient and sustainable renewable energy systems, providing a valuable reference for future research and practical applications within the energy field.

Impact of AI on Enterprise Cloud-Based Integrations and Automation

Artificial Intelligence has transformed enterprise cloud-based integrations and automation, revolutionizing how businesses manage data, workflows, and applications across distributed environments. This comprehensive article explores the impact of AI on enterprise systems, examining key areas, including intelligent data integration, automated workflow optimization, and enhanced security measures. The article delves into technical implementation considerations, discussing infrastructure requirements and integration architectures while highlighting the substantial business benefits in operational efficiency, cost optimization, and strategic advantages. Additionally, it addresses the critical challenges organizations face in technical and organizational dimensions when implementing AI solutions, providing insights into successful adoption strategies and future considerations for enterprise AI integration.

Operation Optimization Framework for Advanced Reactors Using a Data-Driven Digital Twin

Abstract To meet future energy demand, while producing safe, reliable, and carbon-free energy, nuclear reactors will be needed. To make next-generation reactors more economically competitive, the Artificial Intelligence and Machine Learning-based operation optimization module of the dynamic operation and maintenance optimization (DyOMO) framework is proposed. The operation optimization module of DyOMO consists of a data-driven digital twin coupled to a genetic algorithm (GA) optimizer to quickly and efficiently search the solution space for optimal control schemes. The digital twin consists of a Bayesian Network (BN) known as MVCBayes to incorporate uncertainty in the optimization and feedforward neural networks (FFNN) as MVCNet with GA to conduct the optimization. Over time as reactor systems are operating, component degradation will cause the system's electrical output to decrease or be shutdown entirely to perform maintenance. To prevent this, the DyOMO operation optimization module aims to prolong system operation until the next scheduled maintenance period using multiple variable control (MVC) that simultaneously perturbs all actuators to better control the reactor. Comparing this approach with traditional single variable control (SVC), MVC can extend reactor operation past 5% degradation while SVC begins to struggle once the pump and turbine degradation surpasses 0.85% for load-following (LF) operation. Given this extra operation time, the system can continue to run while maximizing its safety margin until the next scheduled shutdown and potentially decrease the total number of maintenance actions throughout the license period to decrease operational and maintenance (O&M) costs.

Hydro–Wind–PV–Integrated Operation Optimization and Ultra-Short-Term HESS Configuration

In order to address the challenges associated with optimizing multi-timescale operations and allocating ultra-short-term energy storage for HWP integration, this study takes into account both the economic and reliability aspects of the HWP integration base. It proposes a model for optimizing operations and allocating energy storage capacity, achieving optimization across long-term, short-term, and ultra-short-term operations for an MECB. Initially, operation optimization is implemented for an entire group of terraced hydropower plants by regulating them with annual regulating capabilities on a long-term timescale. The objectives are to maximize the daily average minimum output and annual power generation. Subsequently, short-term operation optimization focuses on maximizing HWP power feed-in, minimizing new energy power curtailment, and reducing residual load standard deviation while ensuring the guaranteed output optimization results for the long term. Finally, to mitigate ultra-short-term fluctuations in new energy, a HESS with specified capacity and power is configured with the goal of minimizing comprehensive costs. Additionally, to address the challenge of smoothing negative fluctuations, which is hindered by charging and discharging efficiency limitations, a variable baseline is introduced, deviating from the conventional 0 MW baseline. A simulation study based on data from the hydro–wind–PV hybrid project in the Beipanjiang River Basin, China, demonstrates the following: (1) after long-term system optimization, the total power generation capacity of the system increases by 9.68%, while the peak-to-valley difference in output is significantly reduced; (2) short-term system optimization significantly reduces both the average variance in residual loads and the amount of power curtailed over five representative days; (3) the system incorporates 398.62 MWh of lithium-ion battery storage with a power of 412.47 MW and 51.09 MWh of supercapacitor storage with a power of 223.32 MW, which, together, completely smooth out the ultra-short-term fluctuations in new energy output.

Multi-Objective Evolutionary Framework for Layout and Operational Optimization of a Multi-Body Wave Energy Converter

As global energy demand continues to rise, the focus has increasingly shifted toward renewable energy sources. In this context, multi-body Wave Energy Converters (WECs) stand out due to their superior power capture efficiency and enhanced survivability, facilitated by their operational flexibility. Nevertheless, the complexity associated with deploying multi-body WECs in arrays has constrained extensive research in this domain. This study introduces an optimization strategy for the layout design and control of multi-body WECs aimed at efficiently harnessing wave energy. Utilizing Evolutionary Multi-Objective Optimization (EMO) algorithms, this framework optimizes the arrangement and operational parameters of WEC arrays, specifically tailored for the marine conditions off the coast of Oman. The research encompasses two primary optimization phases: layout optimization, which seeks an optimal balance between power production and the separation distance between WEC devices, and operational control optimization, which adjusts WEC configurations according to varying wave conditions. This approach aims to maximize energy capture and ensure sustainable array performance while addressing technical challenges. Simulation results indicate that the optimized configurations not only enhance energy extraction but also significantly reduce hydrodynamic loads, promising improved longevity and efficiency for multi-body WEC systems in extreme marine environments.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

How far back shall we peer? Optimal air handling unit control leveraging extensive past observations

Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring occupant comfort in buildings. Traditional Rule-Based Feedback Control (RBFC) systems, while widely deployed for their simplicity, suffer from low adaptability. Recent improvements, such as Model Predictive Control (MPC) methods demand complex mathematical modeling and substantial expert knowledge, creating a high barrier for system design and optimization. Reinforcement Learning (RL) emerges as a promising solution with its adaptability and model-free nature, albeit challenged by sample inefficiency and suboptimal convergence. Given the intrinsic delayed effects from previous actions and prolonged thermal inertia in the HVAC systems, this study introduces an innovative deep RL framework that can leverage historical observations to refine RL agent performance. By incorporating a state-of-the-art (SOTA) Transformer model, we capture the temporal patterns in HVAC data and build a more precise RL training environment. Implemented on high-resolution, real-world HVAC datasets, our framework showed superior performance in both HVAC system modeling and RL control performance. Specifically, compared to the two baseline models—Bidirectional Long Short-Term Memory (Bi-LSTM) and vanilla Transformer, our proposed RL environment model achieved an average of 30.5% and 35.8% prediction accuracy improvement, respectively. Moreover, our optimal past observable RL agent swiftly delivers 35.3% of electricity saving and 54.4% of thermal comfort improvement over traditional RBFC. These results show the effectiveness of the pioneering integration of extensive historical observations for HVAC operation optimization.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Navigating the Nexus: Unraveling legal and ethical threads in outsourcing for streamlined business processes

The outsourcing practice has increased tremendously over the recent years owing to its positive financial and operational benefits for organizations. However, there are legal and ethical aspects of outsourcing that have garnered a lot of attention. Failure to address the legal and ethical aspects that arise from outsourcing can have far-reaching ramifications including legal battles and losses that may put organizations in difficult situations especially as pertains to accountability to shareholders and customers. This research was aimed at providing comprehensive examination of legal and ethical implication of outsourcing, with key focus on operational optimization. To achieve its objectives, the study employed qualitative and quantitative means to collect data from its sources. The qualitative component utilized interviews and case studies. The quantitative component utilized the survey approach to collect data from professionals in the area of outsourcing. The thematic and statistical analysis of data indicated that there is a strong association between legal and ethical factors on outsourcing. For the outsourcing process to be streamlined, the study recommends that the legal and ethical considerations should be intensified, with emphasis stressed on international legal frameworks and business ethics.

Predictive Analytics for Maintenance

We present the development of a machine learning-based predictive maintenance tool tailored to the industrial industry. Predictive maintenance and operational optimization have turned manufacturing into a manufacturing revolution because to machine learning's capacity to learn from data and generate precise predictions. 96% accuracy was attained in the initial training of a K-means clustering model. An algorithm known as Random Forest was used to increase forecast accuracy, and the outcome was a very good 98% accuracy. Thus, it was decided to apply the Random Forest model. With Flask, a predictive maintenance web interface was created, and the learnt model was easily included to provide real-time predictions. Through dramatically lower equipment downtime and improved operational efficiency, this application highlights the value of machine learning in predictive maintenance in industrial settings.

Resource Efficiency as an environmental performance metric for industry: Exergetic analysis of a clinker manufacturing plant

Control-data exergy-based Resource Efficiency (RE) analysis is applied to investigate resource use and efficiency improvements in the cement industry to understand what insights can be gained into the efficiency of clinker manufacturing and possible decarbonisation options when having full access to material and energy flows from the manufacturing plant control system. Mapping of the resources in and out of the clinker manufacturing section showed that total resource input is dominated by the flow of fuels, with the clinker burning section constituting the most energy and emissions-intensive part of the process. The plant is found to have an annual mean RE of 47.8% in compound operation, and 38.3% in direct operation. Three improvement options are identified: recycling of dust, reduction of heat transfer losses across the kiln, and optimisation of the plant's continuous operation. Heat losses reduction was found to have the largest improvement potential in terms of RE and CO2 emissions reduction. Despite providing a detailed representation of the real-time operation capable of identifying potential improvement interventions, the use of control-data is found to be highly intensive and time-consuming. In the future, it would be interesting to investigate whether the same level of insights into the efficiency and potential for decarbonisation of clinker manufacturing could be gained using less data.

Revisiting the role of thermal energy storage in low‐temperature electrified district heating systems

AbstractDecarbonising the energy supply system is crucial to mitigate climate challenges. An emerging type of the multi‐energy system, that is, the low‐temperature electrified district heating system is gaining increasing popularity as a potential solution for future low‐carbon heat supply. This paper investigated its operational optimisation with thermal energy storage (TES) installed at building sides. The optimisation model was to obtain the minimum operation costs of all heat pumps in this system. The TES was meant to achieve energy arbitrage through load shift, but it was observed from the optimised results that the TES did not play an active role in the optimisation. Five possible causes were identified and further investigated to reveal their impacts on the optimisation process. Results showed that the thermal capacitance, thermal resistance, and indoor temperature range of the building were major influencing factors, while the electricity price tariff and heat loss parameters of TES were minor ones. The results indicate that there is no need to equip the TES for operational optimisation purposes when the building thermal capacitance is larger than a threshold value, the thermal resistance is smaller than a threshold value, or the indoor temperature range is broader than a threshold value. These threshold values are case‐specific and can be determined with the simulation model and method developed in this paper.

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean

The Dark Target (DT) algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi-NPP (SNPP) and NOAA-20 satellites to extend the aerosol data record from the Moderate Resolution Imaging Spectrometer (MODIS). The newly released VIIRS DT Version 2.0 (V2.0) dataset includes NOAA-20 aerosol products for the first time. This study provides the first evaluation of NOAA-20 VIIRS DT aerosol products, including aerosol optical depth (AOD) and Ångstrom exponent (AE), for the period 2018–2022 using Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) measurements. In addition, the operational DT aerosol products from SNPP VIIRS (V2.0) and MODIS (Collection 6.1) are also used for comparison purposes. Overall, NOAA-20 AODs over land and ocean exhibit good validation metrics globally, showing comparability to MODIS products and superiority over overestimated SNPP products. Nevertheless, it is worth noting that in South Africa specifically, NOAA-20 land AODs tends to display more pronounced negative biases, despite all products being underestimated. Furthermore, all ocean AE products demonstrate high correlation coefficients (>0.83) with the ground-based data, meeting the fraction of expected accuracy (>80%). Encouragingly, NOAA-20 AE product has significantly improved the persistent issue of overestimation at low-value in MODIS product, making it a more preferable choice for usage. Error analysis reveals that the performance of all three land products decreases in sparsely vegetated areas and when the solar zenith angle is small. However, NOAA-20 AOD exhibits relatively stable performance and is less affected by variations in aerosol loading, observation geometry, and surface vegetation cover. In addition, the performance of AOD and AE over ocean is significantly influenced by scattering angle and wind speed. This research is anticipated to serve as a reference for the utilization of operational VIIRS DT aerosol products and possible algorithm optimization.

A Combined Investment and Operational Optimization Approach for Power-to-Methanol Plants

In the global effort for industrial decarbonization, repurposing closed coal-fired power plants into power-to-methanol (PtM) plants offers a promising pathway to reduce CO₂ emissions while leveraging existing infrastructure. This study introduces a novel combined optimization approach using mixed-integer linear programming (MILP) to simultaneously optimize the investment and operation of a PtM plant, assessing its economic viability. The model incorporates the operational flexibility of proton exchange membrane (PEM) electrolyzers in response to fluctuating electricity prices through a piecewise linear representation of its load–efficiency characteristic curve. A case study of a repurposed coal plant in Austria demonstrates the model's applicability and practical relevance. The results show that larger electrolyzer capacities, i.e., 434 MW, with flexible part-load operation can significantly reduce methanol production costs, i.e., EUR 0.8/kg, achieving competitiveness under high CO₂ pricing scenarios, i.e., EUR 500/ton. A sensitivity analysis is performed to identify the critical factors influencing production costs. This study concludes that the combined investment and operational optimization approach effectively captures the essential elements of PtM systems, enabling faster, better, and operation-informed investment decisions for innovative technologies to support the ongoing energy transition. These findings indicate that PtM technologies can be a viable solution for asset repurposing, grid stabilization, and decarbonizing hard-to-abate sectors.

Two-stage stochastic robust optimization for capacity allocation and operation of integrated energy systems

To address the challenges posed by various uncertainties in integrated energy systems (IES) for planning and operation, this paper considers the capacity configuration of IES equipment with energy storage systems under a stepped carbon trading mechanism, as well as the planning of electric vehicle (EV) charging stations under different charging modes. A two-stage stochastic robust planning method is proposed, taking into account both short- and long-term uncertainties in renewable energy generation and electric, thermal, and cooling loads. In the first stage, planning decisions are made with the objective of minimizing investment costs, while accounting for the seasonal characteristics of the “source-load” relationship in the planning phase. Stochastic programming is employed to handle long-term uncertainties. In the second stage, operational simulation is performed with the goal of minimizing energy dispatch costs and carbon trading costs, and short-term uncertainties in “source-load” operations are described using robust optimization. The nested column-and-constraint generation (NC&CG) algorithm is used to solve this two-stage model. Finally, the proposed model is applied to an IES in northern China. The results show that orderly EV charging within the IES can reduce both investment and operational costs, as well as carbon emissions. The consideration of a stepped carbon trading mechanism can reduce system carbon emissions and enhance environmental sustainability. IES planning with multiple energy storage types is more economical than with a single energy storage type, and the proposed stochastic robust planning method, which considers both long- and short-term uncertainties, demonstrates stronger reliability and economic performance under extreme conditions.

Robust optimization of regional biomass supply chain system design and operation with data-driven uncertainties

To address climate change and ensure energy security, biomass energy has become a popular renewable energy alternative to traditional fossil fuels. This study focuses on the strategic planning and tactical management problem of a regional biomass supply chain system with multiple feedstock and final products. A novel SVC-based data-driven robust optimization model is developed to provide the tradeoff solutions under uncertainties. Compared with the traditional robust optimization model, it can better depict the uncertain sets, reduce data redundancy, and provide less conservative strategies for decision makers according to their risk preferences. The proposed model is validated through a case study, Meizhou city in China. The results suggested the optimal investment scale and site for biorefineries and biomass power plants at the strategic planning level, as well as the feedstock supply network, inventory management, and production arrangement at the tactic management level. The optimal robust solutions can be derived by varying the envelope level of the uncertain sets. Moreover, sensitivity analysis is performed to investigate the influences of the variations of key parameters. It is found that the produced bioethanol and electricity lack cost advantages in the current situation, even with a 30 % reduction in main cost parameters. Insufficient biomass yield may require extra capital investment to satisfy bioenergy demand but with a higher supply cost. Thus, a high and long-term subsidy is necessary to facilitate the sustainable development of biomass and the low-carbon transition of the energy sector.

Intelligent control system and operational performance optimization of a municipal solid waste incineration power plant

This study proposed an integrated intelligent control system in one municipal solid waste incineration power plant to improve the system's control accuracy, economic performance and environmental impact, including the intelligent combustion, intelligent denitrification, intelligent desulfurization and intelligent soot-blowing control subsystems. The precise detection and adjustment of the key operational parameters was achieved by integrating these modules into the existing distributed control system. A comparative analysis of the operation data between pre-optimization and post-optimization states was conducted to assess the changes in the key operational and economic parameters. The results indicate that the introduction of the intelligent control module can significantly improve the system stability and parameter control accuracy, thereby enhancing the economic efficiency of the waste incineration power plant and reducing the operation workload and the pollutants emissions. Specifically, the standard deviations of main steam flowrate and pressure decreased by 45.1 % and 60.7 %, respectively. Furthermore, the consumptions of the ammonia water and lime slurry were reduced by 38.2 % and 23.2 %, respectively, while the auxiliary power consumption rate declined by two percentage points, and the power generation per ton of waste increased by 4.2 %. These improvements not only strengthen the economic benefits but also effectively reduce the pollutants emissions.