Operational Control Research Articles

Perspectives on risk analysis and control for real-time operation of flood control systems

Flood control operation, a non-engineering measure, can efficiently manage flood disasters within a river basin. However, numerous uncertainties exit in the real-time operation of flood control systems, creating risks in decision-making. As an efficient tool to mitigate these risks, risk management has garnered increasing attention in real-time flood control operation. This communication offers a series of suggestions for future research concerning risk management in real-time flood control operation, including risk assessment, risk diagnosis, and risk control methods.

Robotic irrigation complex for crop cultivation in irrigated areas

Intelligent technologies used in digital agriculture, such as remote sensing, serve as a necessary tool for collecting bio-object data. It has been established that data collected through remote sensing and the Internet of Things (IoT), including comprehensive imaging of agricultural bio-objects, and their subsequent analysis will help solve numerous challenges in agricultural production. (Research purpose) The research aims to develop and validate a robotic irrigation complex for crop cultivation. This system will optimize irrigation processes by considering various environmental factors such as soil moisture, air temperature, wind speed, and other relevant conditions. (Materials and methods) The paper evaluates the signifi cance of developing a design that can optimize the process of crop cultivation and details the applications and databases integrated within the central computing center. The central computing hub monitors the current state of the irrigation area, performs calculations and develops management decisions. Utilizing mobile communications, this hub receives, processes, and transmits commands to the control panels of sprinklers, the pumping station, and the dispatcher for the machine-tractor team. (Results and discussion) The research has validated the design and main components of a robotic irrigation system for crop cultivation. Furthermore, an algorithm has been established to orchestrate the processes of data collection, calculation, and the control and management of operations. It was found that such a complex would optimize the irrigation process, reduce costs and resource consumption in the irrigated area, and decrease environmental pollution. (Conclusions) The implementation of a robotic irrigation complex will reduce water consumption, as well as material and energy costs associated with irrigation. By maintaining soil moisture within optimal ranges, this complex will enable the consistent production of high-quality crops under various weather conditions.

Design and implementation of a nano-scale high-speed multiplier for signal processing applications

Digital signal processing (DSP) is an engineering field involved with increasing the precision and dependability of digital communications and mathematical processes, including equalization, modulation, demodulation, compression, and decompression, which can be used to produce a signal of the highest caliber. To execute vital tasks in DSP, an essential electronic circuit such as a multiplier plays an important role, continually performing tasks such as the multiplication of two binary numbers. Multiplier is a crucial component utilized to implement a wide range of DSP tasks, including convolution, Fourier transform, discrete wavelet transforms (DWT), filtering and dithering, multimedia information processing, and more. A multiplier device includes a clock and reset buttons for more flexible operational control. Each digital signal processor constitutes a multiplier unit. A multiplier unit functions entirely autonomously from the central processing unit (CPU); consequently, the CPU is burdened with a significantly reduced amount of work. Since DSP algorithms must constantly carry out multiplication tasks, the employment of a high-speed multiplier to execute fast-speed filtering processes is vital. The previous multipliers had lots of weaknesses, such as high energy, low speed, and high area, because they implemented this necessary circuit based on traditional technology such as complementary metal-oxide semiconductor (CMOS) and very large-scale integration (VLSI). To solve all previous drawbacks in this necessary circuit, we can use nanotechnology, which directly affects the performance of the multiplier and can overcome all previous issues. One of the alternative nanotechnologies that can be used for designing digital circuits is quantum dot cellular automata, which is high speed, low area, and low power. Therefore, this manuscript suggests a quantum technology-based multiplier for DSP applications. In addition, some vital circuits, such as half adder, full adder, and ripple carry adder (RCA), are suggested for designing a multiplier. Moreover, a systolic array, accumulator, and multiply and accumulate (MAC) unit are proposed based on the quantum technology-based multiplier. Nonetheless, each of the suggested frameworks has a coplanar configuration without rotated cells. The suggested structure is developed and verified utilizing the QCADesigner 2.0.3 tools. The findings showed that all circuits have no complicated configuration, including a higher number of quantum cells, latency, and an optimum area.

Configuration method for medium-deep ground source heat pump system considering renewable energy consumption and smart grid interaction

Medium-deep ground source heat pump (GSHP) systems have the advantages of low carbon, high heat exchange intensity, good thermal storage capacity, and intermittent operation characteristics, which are conducive to renewable energy consumption and grid demand response. The source-side parameters affect the configuration of underground borehole engineering, heat pump units, and other equipment. However, current research mainly focuses on the underground heat exchanger modeling, underground heat exchange, and system operation control, while there are few studies on multi-factor ground source-side parameter design and optimal configuration. In this study, the indicator analyzing the unsteady features and thermal balance state of medium-deep geothermal resources was introduced, and the source-side water temperatures were optimized based on the dynamic characteristics of the geothermal and building sides. A simulation configuration methodology for the source-side flow rate and storage capacity was developed considering the electricity prices, life-cycle cost (LCC), and grid interaction. The optimization of the ground source-side design temperature, flow rate, and capacity parameters of residential and office building heating scenarios were analyzed in northern China, respectively. The results showed that the borehole inlet and outlet temperature can be designed to 5.5 °C/17.5 °C under the annual sustainable state, and the optimal flowrate can be set to 65.77 m3/h, to achieve the lowest cost and highest heating season guarantee rate for the residential scenario. For the office building with optimal system and storage capacity, the average annual LCC is 192,000 yuan/year, with the cumulative peak shaving of 55.8 %, and an increase of 103.3MWh renewable electricity consumption.

Railway Infrastructure Cybersecurity: An Overview

The railway infrastructure constitutes a type of operational technology (OT)-based critical infrastructure, which is expected to work 24x7, 365 days a year, and where the life expectancy of operational equipment often exceeds 30 years. In this domain, an operational anomaly compromising the OT system can cause a train accident or interrupt traffic, with potentially significant impact in terms of business as well as for passenger safety. Due to their relevance, railways are strategic assets of national interest and, consequently, targets of interest for cybercriminals and cyberwarfare activities. For instance, service interruptions may trigger ripple effects resulting in product shortages and widespread supply chain disruptions, with severe impacts for both the economy and national security. In a bid to optimise and streamline operations. the railway industry has recently started taking a series of significant steps towards digitization, with infrastructures experiencing a significant paradigm shift which, for instance, makes it possible to have centralised interlockings and Radio Block Centre (RBC) for an entire country, with geographical redundancy, ensuring the utmost availability and punctuality by moving the control logic to the cloud. Nevertheless, these developments must always be carried on within the scope of established cybersecurity standards and frameworks. This paper presents an analysis of the state of the art on railway cybersecurity, focused on the existing solutions based on the application of the CENELEC “Technical specification 50701 - Railway Application – Cybersecurity”, which is currently the latest European specification addressing railways, being designed to help suppliers, integrators, and operators to implement a cybersecurity risk assessment plan, the necessary controls, and the management of the complete system life cycle. Special attention will be paid to the conduit between the rail signal interlocking system, that controls the line signalling, and the Automatic Train Supervision (ATS) that runs in the Operational Control Centre (OCC), as this has been identified by the European Union Agency for Cybersecurity (ENISA) as one of the most critical systems identified by the operators of essential services.

Surface temperature field real-time reconstruction of hot forging die based on 1DCNN

Accurate reconstruction of the die surface temperature field during the hot forging process is crucial for ensuring optimal control of die temperature and high-quality metal forgings. In this study, we propose a novel method that combines hot forging process parameters with a deep neural network to reconstruct the temperature field on the die surface in real-time. Firstly, a high-fidelity finite element model of the hot forging process is created and validated with a forging experiment. Secondly, temperature observation points are initially selected based on the simulated temperature distribution of the forging die, and the correlation coefficient method is used to optimize the layout of observation points to improve reconstruction accuracy and efficiency. Subsequently, multiple high-quality simulations with varying process parameters are conducted, generating a comprehensive dataset. The 1DCNN algorithm is then presented to produce a mapping from the input to the surface temperature field. Finally, the effectiveness of the surface temperature field reconstruction method is verified. The results indicate that the overall performance of the proposed 1DCNN algorithm is better than the traditional regression algorithms. In terms of reconstruction accuracy, it achieves a prediction accuracy within an absolute error of 3 °C for the outer surface points and within a relative error of 5 % for the cavity region. Regarding reconstruction efficiency, it requires an approximate execution time of 0.064 s for predicting a new sample. This paper presents a method for real-time reconstruction of the die surface temperature field, leveraging hot forging process parameters and temperature data from observation points. This approach enables reliable online monitoring of transient temperature variations on the hot forging die surface, offering valuable support for operational control and maintenance.

Optimality principles in spacecraft neural guidance and control.

This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale.

Assessment of quality indicators in construction

Introduction. Ensuring proper quality of construction is one of the pressing challenges of our time. Consumers expect cheap products from developers in the form of commercial or residential real estate that meet all the required quality indicators. Most accidents in monolithic construction occur due to non-compliance with technological procedures at low temperatures. The purpose of the study is to develop a framework for assessing the quality of concrete work at subzero temperatures (during winter work) based on the results of studies of technological parameters of winter concreting, their impact on the quality and safety of erected monolithic structures of industrial and civil buildings (structures).Materials and methods. The study used the method of approximating distributions of random variables, expert estimates, the theory of multiple principles, and mathematical statistics. The experiments were carried out in the laboratories of the Don State Technical University, as well as in the conditions of actual concrete work on a construction site in winter. The work uses a correlation-regression analysis of the dependence of the organizational and technological causes of inconsistencies (factors) and quality indicators of concrete work production. Statistical information on structural damage and collapse of buildings during their operation was processed. To make organizational and technological decisions to improve the quality of concrete work, it is proposed to take the influence of defects on the load-bearing capacity of buildings as an indicator of the quality level.Results. Using practical and expert methods, the most significant reasons (factors) for the occurrence of inconsistencies in the level of quality of concrete work production have been established. Factors have been identified that have the greatest influence on the change in the load-bearing capacity and reliability of buildings and structures being erected when performing the main types of construction and installation work. The nomenclature of technological quality indicators during winter production of concrete work, their influence on quality substantiated, and methods for assessing the quality of winter concreting technology are developed. Quantitative indicators of the quality of construction of monolithic reinforced concrete structures were obtained.Discussion and conclusions. The practical value of the work lies in the development of a methodological tool for creating a regulatory framework for operational construction control of the production of monolithic and prefabricated monolithic construction and installation works under conditions of exposure to low temperatures, as well as for acceptance control of compliance of completed construction projects with quality requirements. Proposals are given for establishing regulatory tolerances, ways to improve the manufacturability of the equipment used, a new method for designing winter concreting technology, and a quality control method focused on ensuring structural safety have been developed. By developing the research, it is further possible to develop methods for calculating technology parameters taking into account technological variability, as well as establish new indicators to regulate the required level of quality and safety of buildings being built.

Assessment of the effectiveness of mounting the aircraft piston combustion engine on a test stand

The subject of the considerations described in the paper is the problem of efficiency of mounting an aircraft engine on a dyno test bench. The paper presents the empirical research results of vibroacoustic signal application to the evaluation of correctness of positioning of the Rotax 912 piston aircraft engine. The variability of selected parameters and vibroacoustic characteristics for real engine operating conditions was assessed, and functional relations and their discrete representations were developed, reflecting the efficiency of mounting the object on the frame. Thanks to this process, quantitative measures of the diagnostic assessment of the object were obtained, which can be used in periodic or continuous operational control of the object, taking into account the impact of tribological processes taking place in the test object.

Forecast-based and data-driven reinforcement learning for residential heat pump operation

Electrified residential heating systems have great potential for flexibility provision to the electricity grid with advanced operation control mechanisms being able to harness that. In this work, we therefore apply a reinforcement learning (RL) approach for the operation of a residential heat pump in a simulation study and compare the results with a classical rule-based approach. Doing so, we consider an apartment complex with 100 living units and a central heat pump along with a central hot water tank serving as heat storage. Unlike other studies in the field, we focus on a data driven approach where no building model is required and living comfort of the residents is never compromised. Both factors maximize the applicability in real world buildings. Additionally, we examine the effects of uncertainty on the heat pump operation. This is carried out by testing four different observation spaces each with different data visibility and availability to the RL agent. With that we also simulate the heat pump operation under forecast conditions which has not been done before to the best of our knowledge. We find that the inertia of typical residential heat systems is high enough so that missing or uncertain information has only a minor effect on the operation. Compared to the rule-based approach all RL agents are able to exploit variable electricity prices and the flexibility of the heat storage in such a way, that electricity costs and energy consumption can be significantly reduced. Additionally, a large proportion of the nominal electrical power of the installed heat pump could be saved with the presented intelligent operation. The robustness of the approach is shown by running ten independent training and testing cycles for all setups with reproducible results.

Smart Operation Control of Power and Heat Demands in Active Distribution Grids Leveraging Energy Flexibility

Demand flexibility plays a crucial role in mitigating the intermittency of renewable power sources. This paper focuses on an active distribution grid that incorporates flexible heat and electric demands, specifically heat pumps (HPs) and electric vehicles (EVs). Additionally, it addresses photovoltaic (PV) power generation facilities and electrical batteries to enhance demand flexibility. To exploit demand flexibility from both heat and electric demand, along with the integration of PVs and batteries, Control and Communication Mechanisms (CCMs) are formulated. These CCMs integrate demand flexibility into the distribution grids to obtain economic benefits for private households and, at the same time, facilitate voltage control. Concerning EVs, the paper discusses voltage-based droop control, scheduled charging, priority charging, and up-/down-power regulation to optimize the charging and discharging operations. For heat demands, the on-off operation of the HPs integrated with phase change material (PCM) storage is optimized to unlock heat-to-power flexibility. The HP controllers aim to ensure as much self-consumption as possible and provide voltage support for the distribution grid while ensuring the thermal comfort of residents. Finally, the developed CCMs are implemented on a small and representative community of an active distribution grid with eight houses using Power Factory software and DIgSILENT simulation language (DSL). This scalable size of the active distribution network facilitates the careful study of symbiotic interaction among the flexible load, generation, and different houses thoroughly. The simulation results confirm that the integration of flexible demands into the grid using the designed CCMs results in the grid benefiting from stabilized voltage control, especially during peak demand hours.

Development and implementation of technological measures to extend the campaign of blast furnace No. 5 of PJSC Severstal

The work presents generalized experience in the development and implementation at PJSC Severstal of technological measures to extend the campaign of blast furnace No. 5. The authors carried out an analysis, identified and described the problem areas, generalized the principles for ensuring the safety of the shaft lining, boshes and metal receiver of the blast furnace. The results of a study of the working space of blast furnace No. 5 in 2006 are also presented. The identified technological factors ensure an increase in duration of the unit campaign. Technolo­gical measures are given for: washing the blast furnace hearth, reducing chemical erosion of the carbon blocks of the hearth and flange, forming a protective skull in the blast furnace shaft, special methods for loading solid coke substitutes, and organizing an effective structure of the charge column in the blast furnace. It is necessary to use digital models integrated into the blast furnace expert system for operational control of blast furnace technology. The results of the current blast furnace campaign were compared with previous ones. It was proven that the systematic use of all elements of the developed technology makes it possible to achieve high economic indicators while exceeding the standard duration of the campaign by 1.75 times. Experience in technology development made it possible to increase the furnace campaign duration to 17.46 years, achieve a reduction in specific coke consumption by 15.9 %, and increase the specific consumption of natural gas for cast iron smelting by 46.4 %; reduce the specific carbon consumption for cast iron smelting by 6.3 %.

Distribution network voltage control considering virtual power plants cooperative optimization with transactive energy

The integration of massive distributed energy resources (DERs) brings new challenges for the distribution networks (DNs) operation control. To control DERs effectively, virtual power plants (VPPs) are introduced into DNs. Conventionally, the control strategy of VPPs is formulated as an optimization model without considering voltage control and transactive energy (TE) in DNs. In this article, multiple VPPs cooperative optimization with TE is studied and cast as a nonlinear programming model. It aims to optimize the voltage control of DNs and the operation profits of VPPs. To dynamically capture the optimal operation state of VPPs in DNs, the distribution locational marginal pricing and DN partition are incorporated into the model. In general, this type of model is computationally challenging and mainly solved by approximate algorithms with low efficiency. Recently, multi-agent deep reinforcement learning (MADRL) is emerging as a scalable and promising data-driven approach. We propose an augmented MADRL solution to this problem. In the training stage, one soft actor-critic learning agent augmented by parameter sharing strategy is employed to simultaneously capture the state-action patterns of multiple VPPs. The prioritized experience replay technique is applied to improve learning efficiency and speed. Numerical test results demonstrate the superiority of the proposed method over benchmark methods.

About the possibility of protecting UAVs from suppression of control signals

Unmanned aerial vehicles (UAVs) are widely used in a wide variety of sectors, both in the national economy and for military purposes. In the agricultural industry, they are used to monitor farmland, analyze soil samples, and even for livestock grazing. In search and rescue operations, UAVs are used to rescue people from life-threatening situations, and each time this technology proves its usefulness more than ever. UAVs are gradually finding their application in retail trade, transportation, entertainment, home security and even in construction using 3D printers. Simultaneously with the growth of the capabilities of UAVs, methods and means were developed to counteract them. The most affordable method of combating an UAV is to suppress the radio signals used to control it. As a result, the UAV loses contact with the operator, which leads to malfunctions in its operation. Therefore, the relevance of developing methods and means of combating suppression of control signals cannot be doubted and is becoming more and more necessary. The purpose of the work is to develop a scheme of the device (protection unit) that protects the UAV from interference radio signals in the operating mode. The peculiarity of the developed device is that in the absence of interference signals on the standard frequency of the UAV, the protection unit receives the operator's control signal and transmits it to the input of the UAV receiver, which moves in the operating mode and performs the tasks assigned to it. In the event of the appearance of interference radio signals on the standard frequency, it independently detects them and, using the UAV transmitter, issues a command to the operator to change the operating frequency to an additional frequency. At the same time, the output frequency of the protection unit remains constant and is equal to the standard frequency of the UAV. Thus, the protection unit is a simple and inexpensive device that detects interference signals and automatically switches the control of the UAV to an additional operating frequency. A functional diagram of the receiving device was developed, and a detailed description of its operation was provided.

Qubit reset with a shortcut-to-isothermal scheme.

Landauer's principle shows that the minimum energy cost to reset a classical bit in a bath with temperature T is k_{B}Tln2 in the infinite time. However, the task to reset the bit in finite time has posted a new challenge, especially for quantum bit (qubit) where both the operation time and controllability are limited. We design a shortcut-to-isothermal scheme to reset a qubit in finite time τ with limited controllability. The energy cost is minimized with the optimal control scheme with and without bound. This optimal control scheme can provide a reference to realize qubit reset with minimum energy cost for the limited time.

Protuberance placement mastery: Shock wave control integration with Coanda effect to thrust vectoring on a sonic jet

Thrust Vector Control (TVC) is a technique that allows precise control over the direction and velocity of an aerial vehicle. This paper presents a novel approach combining shock wave control and the Coanda effect to apply this technique in sonic flows. The objective is to achieve more eligible and operational control over the deviation angles of the thrust vector. The research is conducted through numerical simulations and experimental tests to investigate the impact of protuberance location (1, 3.5, and 5 mm), protuberance width (1.5, 2 mm), depth of penetration (2, 6, and 10%), and nozzle pressure ratio (ranging from 2.1 to 4) on the flow field, thrust vector angle, and thrust value of the nozzle. The system's behavior depends on the protuberance location, pressure ratio, and penetration depth. The location of the protuberance influences the shocks and flow separation from the Coanda flap. Increasing the penetration depth up to 10% enhances system stability and reduces the protuberance location's influence on flow deviation. The study highlights the significant effect of protuberance placement on thrust loss, with higher penetration depths resulting in a more significant decrease. The system's behavior in terms of flow deviation is more stable at specific penetration depths and pressure ratios. The results show that the proposed technique achieves a maximum deviation angle of 83° for a sonic jet. In addition, the findings contribute to understanding TVC using protuberances and the Coanda effect. The proposed technique offers advantages in simplicity, reliability, and control performance. It opens possibilities for efficient and maneuverable aerospace vehicles, with applications in UAVs, surveillance, and search and rescue missions.

Biochar production using a Flexible Counter Flow Multi-Baffle (F-COMB) reactor

Pyrogenic carbon capture and storage (PyCCS) based on biochar is considered a negative emission technology (NET) aimed at mitigating climate change, with the potential to reduce CO2 by 0.3–2 gigaton/yr at the cost of $30–120/ton-CO2. The annual biochar production needs a substantial scaling up of the current production rates (i.e., >150 million tons by 2050) to meet the above demand. However, current biochar production predominantly relies on a small-scale operation, which generally leads to high operational costs and limited control of the biochar properties. This work presents the operation of a state-of-the-art 1 ton/d pilot-scale Flexible Counter Flow Multi-Baffle (F-COMB) pyrolyzer designed for continuous biochar production, which is a prototype reactor for the scale-up. The F-COMB utilizes counter-flow and vortex mixing in a multi-baffle column structure to enable efficient contact of feedstock with hot gas. It boasts high energy efficiency and economic feasibility through a short residence time, reduced gas flow rate, and simple structure. It produces wood pellet biochar with accurate control of the reaction conditions such as temperature, retention time, hot gas flow rate, and feedstock loading. Moreover, continuous biochar production is done successfully with three flexible baffles configuration, demonstrating the significant potential for the scale-up.

Application of GIS and Artificial Intelligence in Military Operations: Prospects and Challenges

Geographic Information System (GIS) and Remote Sensing have been considered significant in the military due to their spatiality in nature. Recent military developments have seen various military institutions depending on spatial mapping tools, for the purpose of command, control, communication and coordination in military operations. In this study, the qualitativeanalytical method was used to illustrate the applications of GIS in military operations, drawing lessons from land based military developments from selected studies. An online survey was conducted to extract information from a sample of forty (40) students undertaking War Studies at the University of Zimbabwe, who were conveniently selected for the study. In-depth interviews were also done with two (2) military experts from the university. Data collected was analyzed using descriptive statistics and qualitative interpretation of recent developments in military GIS. The benefits of GIS and machine learning in military operations included access to information; improved data management; information dissemination; improved terrain analysis; object, target or pattern recognition; quick data processing algorithms; autonomous systems; and predictive analysis. The study also notes some challenges associated with GIS and machine learning in military operations include expensive to set up which constituted of respondents; training requirements; cyber-security; software issues; offensive in nature, as well as misinterpretation of situations. The study recommends the need for human-AI collaboration and the development of a regulatory framework at national, regional and international level in ensuring sustainable implementation of GIS technology in military operations.

Adaptive Approach as a Tool of Library Management in the Conditions of Turbulent External Environment

The purpose of the article is to define the role, place and possibilities of the adaptive approach in library management within the framework of practical implementation of the concept of sustainable innovative development of the library industry; to study the reasons for applying adaptive management methods in libraries. Research methods. The following methods and approaches were used: general scientific methods of cognition, in particular, analysis and synthesis, abstract and logical, monographic and analytical, decomposition and systematic approach. Using these methods ensured the validity of the provisions of this study. The scientific novelty lies in creating a holistic view of the features, trends and factors of the adaptive approach in library management. Conclusions. A system is considered adaptive if it can adapt to changes in internal and external conditions. Such a system remains operational in the event of unforeseen changes in the properties of the managed object, management goals, or environmental conditions by changing its functioning algorithm, behavioural programme, or search for optimal states. The task of adaptive library management is to keep the library as a system and its processes within the specified limits. The adaptive management system assesses the state of the process within the limits of permissible fluctuations and determines the need for corrective adaptation measures. Fluctuations in the processes of a library institution are expressed in terms of library production. Thus, the use of adaptive management methods meets the practical tasks of sustainable innovative development of the library industry and can be used in the library management tools for its implementation. An adaptive approach to library management is possible in the following cases: a) a developed forecasting system for prompt monitoring of the current situation of the library; b) the possibility of promptly changing plans if they no longer correspond to the new reality; c) an effective system of operational management accounting and control.

Operation optimization of cement clinker production line based on neural network and genetic algorithm

The operation control plays a great role in the running performance of an industrial process. To achieve a successful control, the key point is searching for the target value of the control parameters. The relationship between operation parameters and performance parameters is usually nonlinear and complex, so it is hard to control an industrial process excellently with workers' experience. Based on a large amount of actual operation data, a bridge between control parameters and performance parameters might be built by the neural network. Prediction model is established by using neural network, genetic algorithm was then used to refine these parameters, the optimal condition can be further achieved by combining the relation bridge and genetic algorithm (GA). Paying attention to a cement clinker production process, this article established an optimizing approach based on the neural network and genetic algorithm and one-year operation data (615 sets). The feeding mass rate of raw meal and coal at the precalciner and the feeding coal mass rate at the rotary kiln are selected as the main independent variable and control parameters, and the specific standard coal consumption is determined as the key performance parameter and the optimization objective function. The mean square error and the correlation coefficient of the established neural network model are 12.84 and 0.89, respectively. The relative errors of approximately all prediction data (92.52 %) are within ±5 %. The optimal values for the raw meal feeding rate, the coal feeding rate into precalciner, and the coal feeding rate into rotary kiln are 259.57 t/h, 7.84 t/h, and 7.40 t/h, respectively. In that optimal condition, the specific standard coal consumption reaches 80.00 kgstandard coal/tclinker (This is a relative value, as there is a slight drift in zero point of the factory's instruments). A highly accurate neural network model is developed, which can significantly reduce standard coal consumption and improve industrial energy efficiency.