Resource Efficiency Optimization Research Articles

Green hospitals face to climate change: Between sobriety and resilience

Climate change poses a critical challenge to global health, influencing social and environmental determinants such as housing, air and water quality, and food security. This article explores the profound impact of climate change on health, projecting an additional 250,000 annual deaths from various climate-related diseases between 2030 and 2050. Healthcare systems significantly contribute to global carbon emissions. The concept of the “Green Hospital” is introduced as a paradigm shift in healthcare, focusing on optimizing resource efficiency and minimizing environmental impact. This concept encompasses renewable energy integration, natural lighting, sustainable materials, green roofs, and smart building management systems. Several challenges remain major, such as medical waste management, water conservation, chemical use, pollution, and plastic usage in healthcare settings. Moreover, obstacles to green hospital initiatives should be resolved, including system redundancy, regulatory compliance, operational demands, financial constraints, and cultural resistance. Conclusively, an urgent reformation of healthcare systems is needed to align with eco-friendly and sustainable practices, highlighting the necessity to reduce CO2 emissions and manage resources and waste more effectively to meet the evolving health needs of a growing and aging global population.

ИССЛЕДОВАНИЕ ЭФФЕКТИВНОСТИ ФУНКЦИОНИРОВАНИЯ КИТАЙСКОГО ЗОЛОТОДОБЫВАЮЩЕГО ПРЕДПРИЯТИЯ НА ОСНОВЕ НЕЙРОННЫХ СЕТЕЙ

The article is devoted to the analysis and study of increasing the efficiency of gold mining enterprises. The goal is to develop a tool for quantitative analysis and forecasting of key factors affecting the efficiency of gold mining enterprises. The methodological basis of the work is scientific and practical research methods: the use of economic principles, the combination of econometric models and artificial intelligence algorithms, as well as the results of research by Chinese scientists on the development of gold mining enterprises. During the study, the most significant factors affecting the efficiency of gold mining enterprises were selected. In accordance with the economic principles of optimal resource allocation and efficiency maximization, the creation of a neural network model, using Chinese gold mining enterprises as an example, contributes to the formation of a data set for forecasting and analyzing critical factors affecting the efficiency of the enterprise. By means of the created neural network with fuzzy logic, complex relationships between various factors affecting the efficiency of the enterprise are revealed, and important reference information for enterprises on optimizing resource allocation is obtained. By constructing a neural network forecasting model, it is possible to analyze the future trends of key factors affecting the operating efficiency of an enterprise. Quantitative forecasting and evaluation of each key factor allows enterprises to plan the development and use of resources in advance, and achieve resource allocation with optimal efficiency. It is concluded that the key factors affecting the operating efficiency of gold mining companies are: cost of sales, investment in geological exploration, balance reserves, global demand, operating income, capital expenditure, and investment in social projects. As the global demand for gold continues to grow, Chinese gold mining companies have been constantly increasing their investment in geological exploration and capital expenditure, achieving a continuous increase in core reserves and fully meeting annual production needs.

Smart urban management of green space

This paper aims to explore the concept and applications of smart urban green spaces within the context of sustainable cities. It emphasizes the importance of urban green spaces in providing ecological, social, and economic benefits, such as carbon sequestration, air and water purification, and improved well-being. The paper delves into integrating advanced technologies, including the Internet of Things (IoT), sensor networks, and data analytics, to create smart urban green spaces that optimize resource efficiency and enhance maintenance and operations. Sustainable practices, such as water conservation and biodiversity preservation, are examined for their role in ensuring the long-term viability and resilience of green spaces. The challenges and potential barriers to implementing smart urban green spaces, such as funding and governance issues, are discussed, as well as strategies for overcoming them. Additionally, the paper presents case studies and examples from around the world to showcase successful initiatives in creating smart urban green spaces. By exploring these concepts and applications, this paper contributes to the understanding and advancement of smart urban management of green spaces for sustainable cities.

Sustainability in the metallic minerals industry: The imperative for carbon accounting - insights from firm-level analysis in China

This paper presents an in-depth exploration of critical factors influencing sustainability within China's metallic resource mining sector, examining 100 publicly listed companies within the sector on China's stock exchanges. Using the CS-ARDL technique, the study finds that carbon accounting positively impacts energy expenditure reduction by enhancing transparency on pollution, optimizing resource efficiency, incentivizing energy-efficient technology adoption, and improving market access. Additionally, Information and Communication Technology (ICT) development significantly lowers energy costs in both the short (0.26%) and long term (0.48%) by enabling real-time monitoring, predictive maintenance, automation, and data-driven decision-making. Crucially, Research and Development (R&D) investments reduce energy expenses, with a 1% increase resulting in a 0.16% short-term and 0.36% long-term reduction. Enhanced stock prices correlate with increased energy expenditure, and boosting employment efficiency reduces energy costs. These findings underscore the importance of ICT development, innovation, and comprehensive policy measures in advancing sustainability within China's metallic resource mining companies, aligning with evolving sustainability goals.

Modeling the soil-machine response of secondary tillage: A deep learning approach

Seedbed preparation constitutes a highly important step in the crop establishment process since it directly influences both quality and yield expectation in crop fields. The quality of seedbed preparation is usually assessed by the operator of the tractor or a supervising farmer and thus dependent on qualified field personnel which are scarce today. Subsequent to the visual assessment of the current seedbed structure, a qualified operator can manually adapt the secondary tillage machine in order to optimize resource efficiency (e.g., fuel consumption) while maintaining optimal seedbed preparation according to local soil conditions. In this work, we propose to use an automated approach for real-time in-situ measurement of seedbed quality. Stereo cameras mounted in the front and the back of the tractor provide images and depth-information. Additionally, telemetry data such as working speed, power take-off (PTO) speed, fuel consumption and engine torque utilization of the tractor are logged. Based on this data, we develop a Deep-Learning-based model to predict seedbed quality (measured in form of the Roughness Coefficient), engine torque utilization and engine fuel rate. Thus, we model the interaction of the soil and the machine (soil-machine response) when preparing the seedbed with different machine configurations and environment states. For training and evaluating our model, we collected data from 16 field runs with varying soil types ranging from loamy sand to silty clay in Baden-Württemberg, Germany. Based on this data, we demonstrate that our novel model is able to predict the soil-machine response on previously unseen fields with high accuracy – in our setting with a relative root mean squared error (rRMSE) between 12.2 and 14.4 %. Our proposed model has two main advantages: (1) it allows farmers to plan the optimal machine configuration in advance by simulating different machine configurations and thus reduce the complexity for the operator and (2) it can serve as a basis for the training of intelligent control agents.

Maximizing Oil Palm Yield: Innovative Replanting Strategies for Sustainable Productivity

This paper examines the significance of innovative replanting strategies in maximizing oil palm yield while ensuring sustainable productivity. Through a comprehensive review of literature and analysis of current practices, the major findings of this research highlighted the importance of advanced breeding and clonal selection in developing high-yielding and disease-resistant oil palm varieties. Precision agriculture technologies, including IoT devices, drones, and sensors, were identified as critical tools for data-driven decision making, optimizing resource efficiency, and reducing environmental impact. Sustainable land use planning and agroforestry integration emerged as key strategies to balance productivity with environmental conservation. The broader impacts of this work extend to other agricultural sectors and land use planning, offering valuable insights for policymakers and stakeholders to promote responsible and resilient agricultural practices. By embracing innovative replanting strategies, the oil palm industry can contribute to a more sustainable and prosperous future, balancing economic growth with environmental stewardship. Continued research and collaboration are essential to achieve these goals and foster a harmonious coexistence between productivity and sustainability, integrating precision agriculture technologies for resource optimization and reduced environmental impact, promoting sustainable land use planning and agroforestry integration to enhance biodiversity and ecosystem services. Strengthening collaborations between governments, industry players, and research institutions for innovation and knowledge exchange is essential.

A bi-objective decision support tool based on system dynamics and discrete event modelling for sustainable supply chain

Based on the development trend of sustainable concepts and the implement ability of composites combined with Agave Bagasse Fibre (ABF) and Polyethylene Terephthalate (PET). The purpose of this paper is to design a bi-objective decision support tool for supply chain of composite material which combined with ABF and PET. Through the production, processing, recycling, and reprocessing of composite materials, the sustainable supply chain model of four different schemes is designed, and the data results of each scheme model are calculated and analysed. The tool can support supply chain modelling solutions that seek best practices for sustainable supply chains and optimize resource efficiency through cost and carbon dioxide emissions. The sustainable supply chain model was designed, created, and optimized in AnyLogic software using System Dynamics and Discrete Event Simulation modelling methods based on the supply chain model established by previous researchers. According to the analysis results of the model data, the reasonable design of the whole process can effectively reduce the cost and carbon dioxide emissions and achieve the effectiveness and implementation of the sustainable supply chain. The results of this study will provide reference for more sustainable supply chain models in the future. Further research on composite materials can be carried out by combining with practice.

DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency

The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

Network consensus analysis and optimization of distributed FANETs based on multi-agent consensus theory

Distributed flying ad hoc networks (FANETs) have been widely used in collaborative reconnaissance, situation construction, and other scenarios. In distributed FANETs with multi-hop and intermittent links, nodes only maintain neighbors’ information and cannot obtain the whole network messages. There may be contradicting information collected across nodes, resulting in inconsistency problems. However, existing research on collaborative consensus focuses mainly on the control domain using multi-agent consensus theory. The study on distributed network consensus does not consider the effect of the multi-hop forwarding order, hence limiting the optimization of distributed FANETs. Based on this, we establish a network consensus model utilizing the multi-agent consensus theory and analyze the impact of the outage probability of links and untimely forwarding on the distributed consensus probability, considering the node density, link outage probability, and network maintenance times. Besides, using the election mechanism as an example, we establish distributed network performance analysis models considering consensus error to enhance the service delay and resource efficiency performance analysis of distributed FANETs. Finally, we construct a protocol-level simulation platform based on Visual Studio and extensive experiments to determine the optimal mechanism parameters under different network and channel parameters. The simulation results show that the optimal network maintenance times increase with the increasing outage probability of links. Moreover, distributed FANETs can achieve optimal resource efficiency without achieving complete consensus, that is, there is a tradeoff between network maintenance cost and network performance.

Enhanced meta-heuristic optimization of resource efficiency in multi-relay underground wireless sensor networks.

Achieving a balanced energy and spectral resource utilization is an interesting key design to extend the lifetime of underground wireless sensor networks (UWSNs) where sensor nodes are equipped with small limited energy batteries and communicate through a challenging soil environment. In this article, we apply an improved meta-heuristic algorithm, based on the Salp Swarm Algorithm (SSA), for multi-relay UWSNs where cooperative relay nodes amplify and forward sensed data, received from the buried source nodes, to the aboveground base station. Hence, the optimal nodes transmission powers, maximizing the network resource efficiency, are obtained and used to select beneficial relay nodes. The algorithm enhances the standard SSA by considering the chaotic map for salps population initialization and the uniform crossover technique for salps positions updates. Simulation results show that the proposed algorithm significantly outperforms the SSA in resource efficiency optimization and network lifetime extension. The obtained gain increases when the number of cooperative relay nodes increases. Furthermore, simulations prove the efficiency of the proposed algorithm against other meta-heuristic algorithms.

Enzymatically catalyzed furan-based copolyesters containing dilinoleic diol as a building block.

A more environmentally friendly method for creating sustainable alternatives to traditional aromatic-aliphatic polyesters is a valuable step towards resource-efficiency optimization. A library of furan-based block copolymers was synthesized via temperature-varied two-step polycondensation reaction in diphenyl ether using Candida antarctica lipase B (CAL-B) as a biocatalyst where dimethyl 2,5-furandicarboxylate (DMFDCA), α,ω-aliphatic linear diols (α,ω-ALD), and bio-based dilinoleic diol (DLD) were used as the starting materials. Nuclear magnetic spectroscopy (1H and 13C NMR), Fourier transform spectroscopy (FTIR) and size exclusion chromatography (SEC) were used to analyze the resulting copolymers. Additionally, crystallization behavior and thermal properties were studied using X-ray diffraction (XRD), digital holographic microscopy (DHM), and differential scanning microscopy (DSC). Finally, oxygen transmission rates (OTR) and dynamic mechanical analysis (DMTA) of furan-based copolyesters indicated their potential for medical packaging.

Development of a low-cost iot platform for data collection

Precision agriculture in the Internet of Things (IoT) integrates different technologies able to raise crop productivity, optimize resource efficiency, and accelerate decision making. However, the adoption of this technology is usually costly, affecting the acquisition by the farmers. Thus, the objective of this work was to develop and evaluate low-cost hardware to obtain data in a hydroponic system via IoT. The experiment was conducted at the Pici Campus of the Federal University of Ceará and split into three distinct stages. Firstly, the DS18B20 temperature sensors were calibrated in water, using the KR380 infrared thermometer as a comparison method. For the second step, when the hydroponic system was installed, the water temperature was monitored in the channel and not in the solution reservoir. In this same phase, the quality of data sending and receiving was investigated. In the third step, the sensory data were analyzed with those obtained by the local Meteorological Station. The calibration results revealed that the DS18B20 sensor has reasonable accuracy and excellent agreement and reliability between data. As for receiving and storing, only 6% of the total data was lost.

Knowledge-Based Integration of Product Data in IoT-Platforms to Optimize Resource Efficiency

In times of a global climate crisis, the industrial digitalization offers potential to economize on resources. PDM systems are a viable solution to organize product data in a single, central integration platform. These systems enhance the human workforce by managing product data and integrating modern CAx tools during the product development. Industrial IoT platforms on the other hand gain more significance in the integration and management of smart production processes expanding the data flow over later phases of the product lifecycle.However, between development stages and different data systems the loss of information due to media breaks in the value chain impedes the usage of a holistic data model. The underlying goal is to develop a complete digital representation during all phases of the product development. To expand the digital data model over the whole product lifecycle, the data exchange processes, and the path to a single true data model are the focus of method addressed in this paper.The collection and analysis of data from multiple sources in the production and usage phase have the potential to significantly optimize the resource efficiency over the whole product lifecycle by providing relevant product information at key stages of the product lifecycle. This method aims to close the gap between the two named data systems to expand the digital product representation employing a knowledge-based integration of information.The method develops a well-structured, generic approach to render users independent of provider-specific solutions. The analysis framework includes the phases of product development and production between which the potential for data exchange is shown.

Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

Resource management in Underground Wireless Sensor Networks (UWSNs) is one of the pillars to extend the network lifetime. An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization. This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data, received from the buried source nodes through a lossy soil medium, to the aboveground base station. A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover (HCSSC) algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency. The proposed algorithm improves the standard Salp Swarm Algorithm (SSA) by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of salps. Through experimental results, the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency optimization. Hence, the network's lifetime is prolonged. Indeed, the proposed algorithm achieves an improvement performance of 23.6% and 20.4% for the resource efficiency and average remaining relay battery per transmission, respectively. Furthermore, simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

Resource Efficiency Optimization for Robust Beamforming in Multi-Beam Satellite Communications

The tradeoff between energy efficiency (EE) and spectral efficiency (SE) provides a new perspective for the transmit design in satellite communications (SATCOM), i.e., one can jointly optimize the system EE and SE of the SATCOM system. In addition, jointly considering that it is challenging to obtain accurate channel state information at the transmitter, we study the tradeoff between EE and SE for the robust beamforming in multi-beam SATCOM in this paper. Specifically, with the quality of service and total power constraints taken into account, we study the robust beamforming aimed at maximizing the resource efficiency, which is defined as the weighted sum of the system EE and SE, to achieve the tradeoff between EE and SE with more flexibility. For this problem, we first approximate the ergodic user rate and ergodic signal-to-interference-plus-noise ratio, both with closed forms. Then, the semidefinite relaxation is applied to tackle the optimization of beamforming vectors via relaxing the rank constraints. To handle the sum of fractions involved in the optimization objective, a nested iterative algorithm exploiting the quadratic transformation and majorization-minimization method is proposed. Finally, the eigenvalue decomposition is utilized to obtain the beamformers. Simulation results indicate the effectivity of our proposed algorithm in achieving the EE-SE tradeoff and illustrate the superiority of the robust approach compared with the conventional baselines.

Economic Efficiency of Forest Enterprises—Empirical Study Based on Data Envelopment Analysis

Countries are forced to develop bio-based economic strategies to promote efficient use of renewable natural resources. The transition towards a sustainable forest bio-based economy is associated with resource efficiency optimization, adoption of innovative bio-based approaches in terms of technological improvements and cost effectiveness, and an opportunity to reach multiple societal challenges. This paper is focused on a comparative analysis of the forestry sector in the Republic of Bulgaria and the Slovak Republic by estimating the economic efficiency of four Bulgarian state-owned forest enterprises and four Slovak forest enterprises. The evaluation of economic efficiency was carried out using selected indicators of the studied enterprises over a period of five years. A data envelopment analysis (DEA) approach was used as a non-parametric linear technique for measuring the relative efficiency of a set of production decision-making units (DMUs). The Malmquist productivity index (MPI) was used to assess the pure efficiency changes (PEC) and technological changes (TCs) of the studied forest enterprises. Data for 2014–2018 were processed. The results obtained for the economic efficiency study outlined the major factors affecting the differences in efficiency scores. The long-term sustainability and increased economic efficiency of forest enterprises in both countries can be achieved by improvements in forest management and investments in research and development activities.

Techno‐economic assessment and optimization of a standalone residential hybrid energy system for sustainable energy utilization

Nowadays the whole world is striving to achieve energy security and sustainable development through sustainable utilization of cleaner energy resources. But due to the rapidly rising population caused by the unprecedented urban development, it has created imbalances of energy supply and demand. Hybrid energy systems have emerged as a promising source to augment existing energy infrastructure for sustainable energy supply and to reduce the impact of challenges posed by the energy scarcities. There have been continuous requests from the urban sector for additional energy resources, but the energy generation involves a complex phenomenon. In India also, there are abundances of rising energy demands from various sectors over the regions, and these demands cannot be fulfilled further due to the limitations of the energy generation grids. As a result, an approach is required to offer additional energy resources beyond the use of diesel generator (which increases environmental pollution). Recently due advancement in hybrid energy systems consisting of non-renewable & renewable or renewable & renewable or non-renewable & non-renewable, it provides a scope to understand the various configuration of the hybrid energy system for past trends, present status and future scenarios. The investment in energy technologies is critical to be evaluated with techno-economic feasibility analysis for resource efficiency optimization. Hence, the main objective of this work is to propose an optimal system configuration for urban residential standalone hybrid energy systems to fulfil the electrical energy requirement, which is technically and economically reliable & feasible. The current work accounts for scaling up the existing energy system with proper implementation. The simulations results show that expected average energy requirements of 5 kWh/day with a peak of 818 W for a typical household can be met with proposed configuration (with components like solar PV, wind turbine, power generator [diesel] and battery backup system). The data simulation and optimization technique for a monthly profile of wind, solar, and excess energy generation over HOMER simulation toolkit offer the potential to generate excess electricity of 19.3%. Techno-economic assessment of the system shows that the proposed system offers more economical and performance benefits. This study also articulates the need to propose a better energy system structure with basic energy grid extension as a cost-effective solution for sustainable development. Similar kind of assessment will offer a useful decision support system for tropical countries to guide future researchers in energy studies.

Reducing Lifecycle Costs due to Profile Scanning of the Powder Bed in Metal Printing

First time right is one major goal in powder based 3D metal printing. Reaching this goal is driven by reducing life cycle costs for quality measures, to minimize scrap rate and to increase productivity under optimal resource efficiency. Therefore, monitoring the state of the powder bed for each printed layer is state of the art in selective laser melting. In the most modern approaches the quality monitoring is done by computer vision systems working with an interference on trained neural networks with images taken after exposure and after recoating. There are two drawbacks of this monitoring method: First, the sensor signals - the image of the powder bed - give no direct height information. Second, the application of this method needs to be trained and labeled with reference images for several cases. The novel approach presented in this paper uses a laser line scanner attached to the recoating machine. With this new concept, a direct threshold measure can be applied during the recoating process to detect deviations in height level without prior knowledge. The evaluation can be done online during recoating and feedback to the controller to monitor each individual layer. Hence, in case of deviations the location in the printing plane is an inherent measurement and will be used to decide which severity of error is reported. The signal is used to control the process, either by starting the recoating process again or stopping the printing process. With this approach, the sources of error for each layer can be evaluated with deep information to evaluate the cause of the error. This allows a reduction of failure in the future, which saves material costs, reduces running time of the machine life cycle phase in serial production and results in less rework for manufactured parts. Also a shorter throughput time per print job results, which means that the employee can spent more time to other print jobs and making efficient use of the employee’s work force. In summary, this novel approach will not only reduce material costs but also operating costs and thus optimize the entire life cycle cost structure. The paper presents a first feasibility and application of the described approach for test workpieces in comparison to conventional monitoring systems on an EOS M290 machine.

Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach

The paper presents a structure of the digital environment as an integral part of the “digital twin” technology, and stipulates the research to be carried out towards an energy and recourse efficiency technology assessment of phosphorus production from apatite-nepheline ore waste. The problem with their processing is acute in the regions of the Russian Arctic shelf, where a large number of mining and processing plants are concentrated; therefore, the study and creation of energy-efficient systems for ore waste disposal is an urgent scientific problem. The subject of the study is the infoware for monitoring phosphorus production. The applied study methods are based on systems theory and system analysis, technical cybernetics, machine learning technologies as well as numerical experiments. The usage of “digital twin” elements to increase the energy and resource efficiency of phosphorus production is determined by the desire to minimize the costs of production modernization by introducing advanced algorithms and computer architectures. The algorithmic part of the proposed tools for energy and resource efficiency optimization is based on the deep neural network apparatus and a previously developed mathematical description of the thermophysical, thermodynamic, chemical, and hydrodynamic processes occurring in the phosphorus production system. The ensemble application of deep neural networks allows for multichannel control over the phosphorus technology process and the implementation of continuous additional training for the networks during the technological system operation, creating a high-precision digital copy, which is used to determine control actions and optimize energy and resource consumption. Algorithmic and software elements are developed for the digital environment, and the results of simulation experiments are presented. The main contribution of the conducted research consists of the proposed structure for technological information processing to optimize the phosphorus production system according to the criteria of energy and resource efficiency, as well as the developed software that implements the optimization parameters of this system.