Environmental Controls Research Articles

Climate Control for Extreme Temperatures in Corrections.

Climate Control for Extreme Temperatures in Corrections.

Experimental Investigation on Climate-Control Nano-Composite Polycarbonate Sheets Enhanced with TiO2, ZnO, and Zeolite Nanoparticles for Polyhouse Use

Abstract Optimal food production is achieved when crops receive the necessary nutrients, and the desired levels of temperature, humidity, solar radiation, and sufficient water. Climate Control Agriculture, or Controlled Environment Agriculture, utilizes a technology-based approach to grow plants in a controlled environment. This study focuses on the experimental investigation of polycarbonate sheets doped with TiO2, ZnO, and zeolite nanoparticles for potential use in polyhouse applications. The thermal performance of these synthesized sheets and standard polycarbonate sheets was tested in a laboratory setting. A specialized experimental setup, consisting of two cabinets, was designed and fabricated for this purpose. Ten synthesized sheets each with thicknesses of 50 µm and 70 µm, and two commercial sheets of 70 µm thickness were tested experimentally in the laboratory for their thermal performance. Results indicated that the doping concentration of 0.3% was the most effective, while 0.1% was the least effective. Among the three doping materials, TiO2-doped sheets demonstrated superior ultra-violate light absorption capacity due to their high refractive index and band gap energy, which enhances their ability to absorb and scatter ultra-violate light. . ZnO offered stronger UV protection than the remaining three materials. However, it has a lower refractive index than TiO2. Its overall thermal performance was lower than TiO2. The desired properties of nanocomposites for use in polyhouse applications such as thermal performance in the laboratory, UV protection, mechanical strength and durability, transparency, photostability, and safety were compared based on the experimental results and the data available in the literature and was concluded that polycarbonate doped with ZnO and TiO₂ nanoparticles were the most suitable.

Tectonic and climatic controls on plant biodiversity in the Hengduan Mountains, China

Mountainous regions contain some of the most dynamic landscapes in the world and host a majority of the global biodiversity hotspots. The Hengduan Mountains, located at the syntax of Eurasia and India, have a complex geological history associated with the hyperdiverse assemblage of species. Tectonics and geomorphic and climate processes in the Hengduan Mountains may have jointly caused the emergence of high biodiversity. We propose three mechanisms that may promote high-level montane biodiversity: habitat creation, habitat disruption, and habitat oscillations. We mapped species richness patterns for seed plants and computed phylogenetic endemism across the entire Hengduan region. We developed a set of geomorphic metrics regarding these mechanisms to link plant richness and phylogenetic endemism patterns. By estimating the component of species richness due to habitat heterogeneity, we isolate the component due to tectono-geomorphic processes. We found that regions of exceptionally high species richness are explained by a combination of habitat heterogeneity and tectono-geomorphic processes, while glaciation and high climate change velocity are associated with low richness, thus contributing to recent extinction. We interpret the richness of biodiversity hotspots in the Hengduan Mountains in terms of climatic and tectonic processes.

Beyond the Liver: A Comprehensive Review of Strategies to Prevent Hepatocellular Carcinoma

Background/Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, primarily developing in the context of chronic liver disease. Traditional prevention has focused on liver-specific interventions like antiviral therapies and surveillance. However, extrahepatic factors also significantly contribute to HCC risk. This review explores comprehensive strategies for HCC prevention, including both hepatic and extrahepatic factors. Methods: An extensive literature search of peer-reviewed articles up to October 2024 was conducted, focusing on studies addressing HCC prevention strategies. Studies that focused on both hepatic and extrahepatic factors were included. Data were extracted and synthesized to provide an overview of current prevention strategies and their effectiveness in reducing HCC incidence. Results: Hepatitis B vaccination and antiviral treatments for hepatitis B and C significantly reduce HCC incidence. Lifestyle modifications—such as reducing alcohol consumption, maintaining a healthy weight through diet and exercise, and smoking cessation—are crucial in lowering HCC risk. Environmental measures to limit exposure to aflatoxins and other hazards also contribute to prevention. Regular surveillance of high-risk groups enables early detection and improves survival rates. Emerging strategies like immunotherapy and gene therapy show potential for further reducing HCC risk. Conclusions: A comprehensive approach combining medical interventions, lifestyle changes, and environmental controls is essential for effectively decreasing HCC incidence globally. Implementing these combined measures could significantly reduce the global burden of HCC.

TinyML-powered ensemble modeling for greenhouse climate control using XGBoost and LightGBM

The cultivation of crops in smart greenhouses is experiencing a profound transformation, fueled by cutting-edge technological advancements in environmental control that significantly improve efficiency, sustainability, and productivity. Nonetheless, the intricate and ever-changing dynamics of microclimate conditions pose challenges in customizing environments to satisfy the specific requirements of various plants. Accurate prediction of these microclimate parameters emerges as a promising solution to this challenge. This study explores the integration of machine learning and TinyML platforms to create a groundbreaking ensemble approach for effectively forecasting microclimate conditions. We obtained exceptional prediction accuracy for temperature (R2 = 0.9972) and humidity (R2 = 0.9976) using a stacking ensemble of XGBoost and LightGBM models. We used Optuna for accurate hyperparameter optimization and thoroughly examined the best possible input variable combinations as part of our meticulous model construction approach. The results of this study demonstrate the revolutionary potential of machine learning in greenhouse climate management, opening the door for data-driven, intelligent agricultural systems that maximize crop yields while reducing energy consumption.

Rainmaking: Observed with social systems theory

AbstractThis article examines contemporary efforts to control climate change through the lens of social systems theory, particularly by drawing comparisons to rain dances and shamanic rituals. It argues that modern climate control strategies bear functional similarities to archaic rituals aimed at influencing weather patterns, despite the absence of direct causality. Using Niklas Luhmann's concepts of autopoietic systems and functional equivalence, the article demonstrates that both historical and contemporary approaches to climate influence rely on blame‐shifting mechanisms and the social production of scapegoats, with failure often attributed not to the rituals or solutions themselves but to noncompliance or impurity among participants. The originality of this article lies in its application of social systems theory to link contemporary climate control strategies with ancient rituals, positioning them as functionally equivalent social phenomena. By drawing comparisons between shamanistic practices, corporate consulting, and global climate governance, the article provides a unique lens for understanding that the primary function of modern climate efforts may be to regulate social behaviour rather than achieving concrete natural environmental outcomes.

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

Implementation of thermoelectric wall systems for sustainable indoor environment regulation in buildings through numerical and experimental performance analysis

With buildings representing a substantial portion of global energy consumption, exploring alternatives to traditional fossil fuel-based heating and cooling systems is critical. Thermoelectricity offers a promising solution by converting temperature differentials into electrical voltage or vice versa, enabling efficient indoor thermal regulation. This paper presents a comprehensive investigation into the integration of thermoelectric wall systems for sustainable building climate control through numerical simulations and experimental analyses. Numerical simulations using computational fluid dynamics (CFD) techniques were conducted to model fluid flow and heat transfer within the thermoelectric wall systems under various operating conditions. These simulations provided insights into the system’s thermal behavior, which were validated through experimental setups designed to measure temperature differentials, airflow rates, and power consumption. The results showed that power consumption is directly correlated with electrical current, ranging from 0.19 W to 77.4 W as the current increased from 0.1 A to 2 A. Additionally, the heat absorbed by the system increased significantly with electrical current, by 706–1044%, depending on the air velocity. The thermal energy released from the hot side of the thermoelectric modules also rose substantially, ranging from 9850 to 5285% with increasing electrical current, and from 275 to 51% with higher air velocities. Moreover, increasing air velocity led to a 6.78–9.37% reduction in power consumption for currents between 0.1 A and 2 A. The coefficient of performance (COP) analysis revealed that optimizing both electrical current and air velocity is essential for maximizing system efficiency. While fan power consumption reduces COP at higher air velocities, neglecting fan power consumption results in COP improvements ranging from 6.5 × 10⁻⁴ to 49.0%. These findings highlight the potential of thermoelectric wall systems to enhance indoor comfort and energy efficiency in buildings.

Optimizing Fermentation Strategies for Enhanced Tryptophan Production in Escherichia coli: Integrating Genetic and Environmental Controls for Industrial Applications

Tryptophan is an essential aromatic amino acid widely used in the pharmaceutical, agricultural, and feed industries. Microbial fermentation, mainly using Escherichia coli, has become the preferred method for its production due to sustainability and lower costs. Optimizing tryptophan production requires careful control of various fermentation parameters, including nutrients, pH, temperature, and dissolved oxygen (DO) levels. Glucose, as the primary carbon source, must be fed at controlled rates to avoid metabolic overflow, which leads to by-product accumulation and reduced production efficiency. Nitrogen sources, both organic (such as yeast extract) and inorganic (like ammonium), influence biomass growth and tryptophan yield, with ammonium levels requiring careful regulation to avoid toxic accumulation. Phosphate enhances growth but can lead to by-product formation if used excessively. pH is another critical factor, with an optimal range between 6.5 and 7.2, where enzyme activity is maximized. Temperature control promotes growth and production, particularly between 30 °C and 37 °C. High DO levels increase tryptophan titers by boosting the pentose phosphate pathway and reducing by-products like acetate. Furthermore, surfactants and supplements such as betaine monohydrate and citrate help alleviate osmotic stress and enhance precursor availability, improving production efficiency. Careful manipulation of these parameters allows for high-density cell cultures and significant tryptophan accumulation, making microbial fermentation competitive for large-scale production.

Experimental Study on the Performance of a Novel Data Center Air Conditioner Combining Air Cooling and Evaporative Cooling

Addressing challenges of high-temperature shutdowns, low energy efficiency, and limited natural cooling utilization in data center air conditioning, a novel hybrid air cooling and evaporative cooling air-conditioning(ACEC-AC) system has been developed. The system is well-suited for application in data centers located in regions with high outdoor temperatures and large dry-bulb to wet-bulb temperature differences during summer. Experimental evaluations in an enthalpy difference laboratory revealed that the hybrid system's performance in wet mode (combining air cooling and evaporative cooling) significantly outperforms its dry mode (solely air cooling) and traditional cooling units. At an outdoor temperature of 35.60°C dry-bulb and 29.95°C wet-bulb, the system achieved a COP of 3.85, a 21.45% improvement over dry mode and 16.16% higher than conventional Packaged computer room air conditioning (CRAC). Notably, the system maximizes its benefits in regions like Guangzhou, where the temperature difference between the dry bulb and wet bulb is significant, resulting in an annual operational cost savings of over 34.16%. In conclusion, the hybrid air cooling and evaporative cooling system represents an effective energy-saving technology with great potential to enhance data center performance and reduce energy consumption.

A Northgrippian sedimentary magnetic enhancement along the western margin of India

Sedimentary magnetic records of shelf region contains complex environmental evolution information, necessitating records from various global regions to accurately interpret its significance. A sediment core (SSD-39/GC-01) retrieved from the eastern Arabian Sea was analyzed to elucidate the geologic and climatic controls on the sediment rock magnetic and grain size properties. We report the first record of high energy sediment deposits linked with intense monsoon phases, sea level variations, and extreme natural events during the Northgrippian period (8.27 ka to 4.20 ka). The compilation of magnetic susceptibility profiles of six sediment cores from the western margin of India revealed the presence of multiple sedimentary intervals of enhanced magnetic susceptibility and most likely reflect periods of high sedimentation events controlled by the regional geologic and climatic processes. We identified two anomalous sedimentary magnetic zones (Z-II, Z-IV) marked by an elevated magnetite content and sediment grain size, which reflect the periods of high-sedimentation events on the shelf off Goa. A shift in the magnetic mineral composition, clastic grain size, calcium carbonate, and organic carbon content at ~1.8 ka (Z-I) indicate a abrupt change in monsoon intensity. The elevated organic content within Z-I indicate efficient preservation of labile organic matter which survived oxidation due to rapid sediment deposition. End-member modeling of rock magnetic and grain size properties enabled us to discriminate and quantify the contributions of terrigenous fluxes and post-depositional mineral phases to the bulk magnetic mineral assemblage. We demonstrate that rapid scanning of magnetic susceptibility of sediment cores has the potential to precisely detect the periods of increased continental (magnetic flux) inputs into the marine shelf system. The proposed magnetic mineralogical approach has wider scope to constrain the understanding of how shelf sedimentation responded to past geological and climatic conditions globally.

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Climatic Influence on the Carotenoids Concentration in a Mediterranean Coastal Lagoon Through Remote Sensing

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends.

Removal of moisture from air by cooling method

Moisture removal from air is a critical aspect of climate control in various industrial and residential applications. The cooling method, which relies on the condensation of water vapor by lowering air temperature below its dew point, is one of the most commonly used techniques for dehumidification. This study investigates the principles, effectiveness, and limitations of the cooling method in different environmental conditions. By examining the impact of factors such as ambient temperature, humidity levels, and cooling system design, this research aims to identify strategies for optimizing energy efficiency in dehumidification processes. Experimental results highlight the correlation between cooling temperature and condensation rates, emphasizing the need for advanced cooling technologies to reduce energy consumption. The findings contribute to the development of more sustainable and cost-effective dehumidification systems, offering valuable insights for enhancing HVAC performance and reducing environmental impact.

Effect of sustainable food habits on gut microbiota diversity: A pilot study

Abstract Background To reach climate goals, sustainable food habits are needed and a plant-based diet is more climate-friendly. However, analyses of food products show that the highest levels of pesticide residues have been found in vegetables, fruits and cereal. Further, pesticide residues have been seen to decrease microbiota diversity. Since microbiota diversity is linked to human health, understanding these dynamics is crucial. This study aimed to evaluate the effect of sustainable food habits on gut microbiota diversity. Methods An 8-week intervention study on sustainable food habits with participants randomized into three groups and assigned to different diets; climate friendly (n = 8), organic (n = 9) and control group (n = 9). Data was collected at baseline, at 4 weeks, and at 8 weeks and included a comprehensive meal questionnaire (Meal-Q), urine samples for pesticide analysis, and fecal samples for gut microbiota diversity. As intervention the groups attended four educational sessions about their specific diet. Linear mixed-effects models were used for data analyses. Results There was no indication of adverse effects of a climate friendly diet (p = 0.79) or an organic diet (p = 0.32) on gut microbiota diversity compared to the control group. Pesticide residues that showed negative effects on gut microbiota diversity were mepiquat (p < 0.05) and chlormequat (p < 0.05). The single food items that had adverse effects on gut microbiota diversity was Lemonade (p < 0.05), Juice (p < 0.05), and Brie cheese (p < 0.05). Conclusions Despite its small sample size, this study provides valuable insights, supported by an 8-week longitudinal design and comprehensive monitoring of dietary habits. Notably, both climate-friendly and organic diets showed trends toward increased gut microbiota diversity over time. These findings need to be confirmed in future studies with longer durations and larger sample size. Key messages • A climate-friendly diet not only benefits planetary health but also holds potential for individual health improvements. • Increased intake of plant-based items did not reduce gut microbiota diversity.

Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient

ABSTRACTIn the context of rising global temperatures and their impact on weather patterns and water cycles, understanding the relationship between vegetation and hydroclimatic forcing is critical. Riparian forests are highly vulnerable to hydroclimatic variability, which can significantly affect water availability in the soil on which they primarily depend. Along large rivers, hydroclimatic forcings can vary, resulting in different vegetative responses depending on the local climatic context and site conditions. To explore this, we studied riparian forest greenness along a 512‐km river corridor with a 3° latitudinal gradient, analysing the relative contributions of climate (latitude, season, temperature, precipitation) and local hydrological conditions (groundwater). Here, we show that riparian forests along a latitudinal gradient respond differently to hydroclimatic controls, with vegetative dynamics that can be attenuated or accentuated by local site conditions. We combined Sentinel‐2 satellite Normalised Difference Vegetation Index (NDVI) data over seven years (2016–2022) with hydroclimatic data to examine riparian forest greenness responses to latitudinal, seasonal and interannual hydroclimatic variability. We found contrasting hydroclimatic controls across the latitudinal gradient, with the northernmost sites predominantly controlled by temperature, while those further south are limited by water availability. In addition, we identified temperature as the primary driver of NDVI throughout the growing season, either positively or negatively. Late season precipitation and high phreatic water availability positively influenced NDVI, emphasising the role of local conditions in governing riparian forest resilience. This study enhances understanding of climate controls on riparian tree greenness, which is critical for designing effective landscape‐scale riparian ecosystem management and restoration strategies.

The Presence of Heavy Metals in Drinking Water and Its Possible Impact on the Development of Type 1 Diabetes in Children

This study aimed to determine the relationship between the concentration of heavy metals in drinking water and the number of type 1 diabetes mellitus (T1DM) cases in two regions of Poland. The number of births in 2015-16 in Pomeranian Voivodeship was 50,461, while the number of new T1DM cases was 219. In Lublin Voivodeship, the number of births in the same period reached 39,381, and the number of new T1DM cases was 221. The incidence of T1DM, calculated per 100 live births, amounted to 0.43 and 0.56 in Pomeranian and Lublin. The statistical analysis of collected data proved that the number of new T1DM cases in Pomeranian Voivodeship is correlated to the concentrations of selenium (Se; p < 0.0001), lead (Pb; p < 0.00001), cadmium (Cd; p < 0.00001), zinc (Zn; p < 0.00001) and arsenic (As; p = 0.00001). In the case of Lublin Voivodeship, the number of new T1DM cases was correlated to the concentrations of Se (p = 0.0000001), Pb (p < 0.000001), Cd (p = 0.0000001) and Zn (p < 0.00001) in drinking water. No correlation was found between the number of new T1DM cases and arsenic concentration in the drinking water samples from Lublin Voivodeship. The results indicated that environmental exposure to specific heavy metals may contribute to the risk of T1DM, underscoring the need for stringent environmental controls and public health policies to mitigate these risks.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Climatic and edaphic controls of root-tip production and mortality in five temperate tree species

Root tips are the main components of absorptive fine roots, but their seasonal dynamics and relationship to environmental factors remain unclear due to the difficulties in methodology. In this study, we explored the temporal patterns of root-tip production and mortality in monoculture plantations of five temperate tree species at a common site in northeastern China, and identified the general environmental controls on such processes. We made monthly in-situ assessments of root tip length (RTL) production and mortality in two hardwood and three coniferous species with a minirhizotron (MR) method during the growing seasons of 2008 and 2009. Air temperature, rainfall, soil temperature and water content at 10 cm depth were determined concurrently. RTL production in all species exhibited consistent peaks in summer (June–August) in two growing seasons. RTL mortality showed substantial interannual and interspecific variability, with peaks in autumn and winter in 2008, but various patterns in 2009. RTL production positively correlated with monthly soil and air temperature across all species, and with monthly rainfall in three coniferous species. However, there was no significant correlation between RTL production and soil water content. By contrast, RTL mortality was weakly related to environmental factors, showing positive correlations with soil temperature in Korean spruce, and with rainfall in Korean pine and Korean spruce. Our findings suggest that the seasonal patterns of RTL production are convergent across the five temperate tree species due to the overlapped distribution of heat and rainfall, which can conduce roots to maximizing the acquisition of nutrient resources in the soil.

MODELING OF THE AUTOMATED COMPLEX COMPONENTS FOR SEGREGATED RESIDUES GATHERING OF OPERATIONAL PRINTING

Abstract. An overview of the printing industry primary measures regarding the balanced implementation of the sustainable development goals at the regional level was carried out. Based on the latest works analysis in the direction of the enlargement of branch waste management systems, the solutions insufficiency for the limited financial situations of small businesses is revealed, and the relevance of finding optimal means of gathering segregated waste and their temporary storage at the enterprise is shown. The criteria have been established and the stratification of typical categories of orders the operational printing has been carried out according to print run and rest volume. The requirements for the collection and storage of waste raw materials and consumables have been clarified and expanded, in particular regarding the provision of microclimatic conditions. In order to model effective business processes of pro­duction residues management, the construction of a universal complex for target cumulative of pre-segregated substrate scraps in the form of a smart container with a weighing terminal is substantiated. For flexible allocation of corporate flows, the hardware part is implemented from measuring, supervisory and communication blocks depending on their purpose in the automated microprocessor platform. The researched printing waste types the built algorithm of the life cycle of their cumulating made it possible to draw up an architectural diagram of segregator system and determine the main components hierarchy of printing waste receptacle when order manufacturing. As a result, analytical models were built and the modeling of automated complex of segregated residues gathering in operational printing was carried out based on created microcontroller system for monitoring of mass and climate control of cumulated surpluses to maintenance proper conditions their subsequent recycling.