Plant Efficiency Research Articles

Innovative and efficient integrations of desalination plants coupled absorption, adsorption, and humidification-dehumidification desalination units employing external heat recovery techniques

Thermal desalination technologies have been developed recently to solve some pressing problems, such as high energy expenditure, increasing fuel costs, and environmental problems related to conventional plants. However, these technologies still need to be enhanced in terms of water production and gain output ratio (GOR). This paper presents innovative and efficient combined thermal desalination plants consisting of absorption (ABDS), adsorption (ADDS), and humidification-dehumidification (HDH) desalination units to enhance energy utilization, water production, and overall system performance. Four innovative combined desalination plants employing different external heat recovery scenarios are presented and compared. The result from each scenario is evaluated and compared with those of the standalone ABDS and with other studies found in the literature at the same heat source temperatures. The heat source is mainly utilized to drive the ABDS plant. The ADDS and HDH are driven by the heat rejection from the ABDS components with different external heat recovery scenarios. Results illustrated that the maximum freshwater production generated from the proposed scenarios ranges between 1.57 and 2.94 m3.day−1 with a GOR of between 1.38 and 1.75 at 120 °C. Raising heat source temperatures from 55 to 120 °C decreased the improvement in the water productivity from 618.5 to 224 % and decreased the enhancement in the GOR from 222.8 to 99 %. In scenarios where the ADDS and HDH are driven by the heat rejection from the absorption and condensation process of the ABDS, the maximum freshwater production results from the ABDS unit. On the other hand, in scenarios where the ADDS and HDH are driven by the outlet hot water from the ABDS-generator, the maximum freshwater production is obtained from the HDH unit. The finding provided that the proposed innovative scenarios have an effective improvement in overall system performance indicators.

Automated Early Detection of Plant Diseases a Machine Learning Approach

This study introduces an innovative strategy for the early detection of plant diseases leveraging machine learning algorithms, a crucial step towards bolstering global food security. Despite the importance of early disease detection for mitigating crop losses, inadequate infrastructure often hinders swift and accurate identification. This paper addresses these challenges by utilizing the Random Forest algorithm to distinguish between healthy and diseased plant leaves using a specially curated dataset. Our proposed method involves comprehensive phases of dataset creation, feature extraction, classifier training, and subsequent classification. For the extraction of image features, the Histogram of Oriented Gradient (HOG) technique is utilized, offering a robust method for image analysis. Embracing the capabilities of publicly accessible, large-scale datasets, our method provides a scalable solution to plant disease detection. The empirical results demonstrate promising levels of accuracy in disease monitoring, presenting transformative potential for disease management practices in agriculture. Particularly in resourceconstrained settings, our machine learning approach paves the way for efficient and effective plant disease detection, carrying significant implications for future crop protection strategies.

Optimization of polyamine and mycorrhiza in sorghum plant for removal of hazardous cadmium

Eco-friendly and sustainable practices must be followed while using the right plants and microbes to remove harmful heavy metals from the soil. The goal of the current study was to ascertain how effectively sorghum plants removed cadmium (Cd) from the soil using polyamines and mycorrhiza. Plant-biochemicals such as free amino acids, ascorbic acids, anthocyanin, proline, and catalase, APX, peroxidase activities were considered as markers in this study which revealed the adverse plant growth performance under 70 and 150 ppm of Cd concentration (w/w) after 30,60, and 90 days of treatment. The plants showed a mitigating effect against high Cd-concentration with exogenous use of mycorrhiza and putrescine. The treatment T17 (mycorrhiza +5 mM putrescine) showed a substantial decrease in the content of total free amino acid, ascorbic acid, catalase, APX, peroxidase by 228.36%, 39.79%, 59.06%, 182.79% 106.97%, respectively after 90 days as compared to T12 (150 ppm Cd). Anthocyanin content was negatively correlated (−0.503, −0.556, and −0.613) at p < 0.01 with other studied markers, with an increase by 10.52% in T17 treated plant as compared to T12. The concentration of Cd in root increased by 49.6% (141 ppm) and decreased in the shoot by 71% (17.8 ppm) in T17 treated plant as compared to T12 after 90 days. The application of mycorrhiza and putrescine significantly increased BCF (>1) and decreased TF (<1) for Cd translocation. The administration of mycorrhiza and putrescine boosted the Cd removal efficiency of sorghum plants, according to FTIR, XRD, and DSC analysis. As a result, this study demonstrates novel approaches for induced phytoremediation activity of plants via mycorrhiza and putrescine augmentation, which can be a promising option for efficient bioremediation in contaminated sites.

Wastewater treatment plant efficiency and contaminant levels in a Mediterranean coastal area: a comprehensive inventory and assessment

Wastewater treatment plant efficiency and contaminant levels in a Mediterranean coastal area: a comprehensive inventory and assessment

A novel multi-model estimation of phosphorus in coal and its ash using FTIR spectroscopy

The level of phosphorus must be carefully monitored for proper and effective utilization of coal and coal ash. The phosphorus content needs to be assessed to optimize combustion efficiency and maintenance costs of power plants, ensure quality, and minimize the environmental impact of coal and coal ash. The detection of low levels of phosphorus in coal and coal ash is a significant challenge due to its complex chemical composition and low concentration levels. Effective monitoring requires accurate and sensitive equipment for the detection of phosphorus in coal and coal ash. X-ray fluorescence (XRF) is a commonly used analytical technique for the determination of phosphorus content in coal and coal ash samples but proves challenging due to their comparatively weak fluorescence intensity. Fourier Transform Infrared spectroscopy (FTIR) emerges as a promising alternative that is simple, rapid, and cost-effective. However, research in this area has been limited. Until now, only a limited number of research studies have outlined the estimation of major elements in coal, predominantly relying on FTIR spectroscopy. In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. For model development, the methodology employs the mid-infrared absorption peak intensity levels of phosphorus-specific functional groups and anionic groups of phosphate minerals at various working concentration ranges of coal and coal ash. This paper proposes a multi-model estimation (using PLR, PLSR, and RF) approach based on FTIR spectral data to detect and rapidly estimate low levels of phosphorus in coal and its ash (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} of 0.836, RMSE of 0.735 ppm, RMSE (%) of 34.801, MBE of − 0.077 ppm, MBE (%) of 5.499, and MAE of 0.528 ppm in coal samples and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} of 0.803, RMSE of 0.676 ppm, RMSE (%) of 38.050, MBE of − 0.118 ppm, MBE (%) of 4.501, and MAE of 0.474 ppm in coal ash samples). Our findings suggest that FTIR combined with the multi-model approach combining PLR, PLSR, and RF regression models is a reliable tool for rapid and near-real-time measurement of phosphorus in coal and coal ash and can be suitably modified to model phosphorus content in other natural samples such as soil, shale, etc.

Enhancing rangeland weed detection through convolutional neural networks and transfer learning

The detection of weed species in rangeland environments is a challenging task due to various factors such as dense, variable species vegetation, ocular occlusion, and a wide variety of plant morphology. Most research in weed detection, however, focuses on croplands. This research addresses the need for accurate rangeland weed detection models by leveraging convolutional neural network (CNN) models enhanced with transfer learning applied to the DeepWeeds data set taken in situ in regional North Eastern Australia. It investigates the effectiveness of transfer learning across seven popular models, utilizing data augmentation and fine-tuning. The performance of these models was evaluated using accuracy metrics and compared against each other. The results demonstrated that transfer learning, coupled with fine tuning, could be a viable solution for generating efficient weed plant detection models with lower demands on computational resources and smaller datasets, despite the challenging conditions of rangeland environments. EfficientNetV2B1 had the highest classification accuracy of 94.2 ​%, and lowest training times. Moreover, high levels of accuracy were also achieved using InceptionV3, VGG16, and Densenet121, albeit with a training time penalty. This research provides insights into the performance of CNN models in challenging rangeland environments, demonstrates the potential of using transfer learning to enhance weed detection models, and underscores the significance of model selection in agricultural applications of CNNs.

ABA-receptor agonist iSB09 decreases soil water consumption and increases tomato CO2 assimilation and water use efficiency under drought stress

Climate change can alter precipitation patterns, disrupting the natural water cycle and generating drought periods that negatively impact crop yield or plant survival. Novel biotechnological approaches are being developed to face water deficits. Specifically, molecular knowledge of the plant hormone abscisic acid (ABA) can be harnessed to develop genetic and chemical approaches to cope with abiotic stress. ABA receptor agonists are promising molecules that activate ABA signaling on demand and show long-lasting effects, in contrast to the exogenous application of ABA, which has a short half-life. In this work, we studied the effect of the iSB09 agonist on tomato plants grown under drought stress or well-watered conditions. iSB09 treatment induced stomatal closure in tomato through activation of PYL1-like and PYL4-like ABA receptors. Additionally, RNA-seq analyses reveal coordinated upregulation by ABA or iSB09 of the genes encoding enzymes involved in the synthesis of the osmoprotective galactinol and raffinose family of oligosaccharides. Foliar spraying of iSB09 under drought conditions anticipated the regulation of transpiration, promoted drought avoidance and increased water use efficiency in tomato plants. Physiological analysis of agonist-treated plants reveals increased CO2 assimilation and effective quantum yield of the photosystem II under drought conditions in iSB09-treated plants compared to mock-treated. Faster regulation of transpiration at the start of the drought period was achieved by iSB09 treatment, and, as a result, water consumption was reduced compared to mock-treated plants. Overall, the agonist treatment mounts the genome-wide transcriptional response to stress and increases water use efficiency under drought conditions and plant protection.

Incorporation of compost and biochar enhances yield and medicinal compounds in seeds of water-stressed Trigonella foenum-graecum L. plants cultivated in saline calcareous soils

BackgroundThe combination of compost and biochar (CB) plays an important role in soil restoration and mitigation strategies against drought stress in plants. In the current study, the impact of CB was determined on the characteristics of saline calcareous soil and the productivity of fenugreek (Trigonella foenum-graecum L.) plants. The field trials examined CB rates (CB0, CB10 and CB20 corresponding to 0, 10, and 20 t ha‒1, respectively) under deficit irrigation [DI0%, DI20%, and DI40% receiving 100, 80, and 60% crop evapotranspiration (ETc), respectively] conditions on growth, seed yield (SY), quality, and water productivity (WP) of fenugreek grown in saline calcareous soils.ResultsIn general, DI negatively affected the morpho-physio-biochemical responses in plants cultivated in saline calcareous soils. However, amendments of CB10 or CB20 improved soil structure under DI conditions. This was evidenced by the decreased pH, electrical conductivity of soil extract (ECe), and bulk density but increased organic matter, macronutrient (N, P, and K) availability, water retention, and total porosity; thus, maintaining better water and nutritional status. These soil modifications improved chlorophyll, tissue water contents, cell membrane stability, photosystem II photochemical efficiency, photosynthetic performance, and nutritional homeostasis of drought-stressed plants. This was also supported by increased osmolytes, non-enzymatic, and enzymatic activities under DI conditions. Regardless of DI regimes, SY was significantly (P ≤ 0.05) improved by 40.0 and 102.5% when plants were treated with CB10 and CB20, respectively, as similarly observed for seed alkaloids (87.0, and 39.1%), trigonelline content (43.8, and 16.7%) and WP (40.9, and 104.5%) over unamended control plants.ConclusionsOverall, the application of organic amendments of CB can be a promising sustainable solution for improving saline calcareous soil properties, mitigating the negative effects of DI stress, and enhancing crop productivity in arid and semi-arid agro-climates.

Utilization of organic-residues as potting media: Physico-chemical characteristics and their influence on vegetable production.

Soilless agriculture is acknowledged worldwide because it uses organic leftovers as a means of supporting intensive and efficient plant production. However, the quality of potting media deteriorates because of lower nutrient content and excessive shrinkage of most organic materials. A current study was undertaken to identify the optimal blend of locally available organic materials with desirable qualities for use as potting media. Therefore, different ingredients, viz., Pinus roxburghii needles, sugarcane bagasse, and farmyard manure were used alone or in combination as potting media to test their suitability by growing spinach as a test crop. Results showed that an increase in Pinus roxburghii needles and sugarcane bagasse decreased medium pH and electrical conductivity. Higher pH and electrical conductivity were recorded for the treatments having a higher farmyard manure ratio (≥50%) in combination. Except for pine needles 100%, pH and electrical conductivity were in the recommended range. The growth attributes include, leaves plant-1, shoot length, fresh- and dry shoot weight along with plant macronutrients (nitrogen, phosphorous, and potassium) and micronutrients (iron, copper, manganese, and zinc) content were higher in treatment pine needles 50%+farmyard manure 50% followed by pine needles 25%+farmyard manure 50%+sugarcane bagasse 25%. Moreover, the particular treatment of pine needles 50%+farmyard manure 50% exhibited the highest concentrations of macro- (nitrogen, phosphorus, and potassium) as well as micronutrients (iron, copper, manganese, and zinc) in the potting media following the harvest. This study highlights the potential of utilizing agro-industrial litter/waste as a soilless growing medium for spinach production under greenhouse conditions. When employed in appropriate proportions, this approach not only addresses disposal concerns but also proves effective for sustainable cultivation. Further research is needed to investigate the use of these wastes as potting media by mixing various particle-size ingredients.

Energy efficiency evaluation and optimization for wastewater treatment plant

Wastewater treatment plants (WWTPs) are considered as energy-intensive industries. A comprehensive assessment of energy efficiency in sewage treatment reveals issues of energy waste, offers insights into the energy consumption structure, fosters optimization of energy management, and enhances overall energy utilization. However, the mathematical modeling for energy efficiency evaluation becomes challenging due to the ambiguity and uncertainty of the parameters regarding energy consumption and various indicators. In this paper, the newly constructed evaluation method was used to quantify the energy efficiency. This method employs dimensional reduction, projecting the numerical values of unit energy consumption for various pollutants onto a lower-dimensional space, and then computing the projected eigenvalues. Larger eigenvalues indicate higher energy efficiency. Based on the evaluation results, the back propagation (BP) neural network is used to simulate the sewage treatment process. The results demonstrated that when the inflow load is small and the concentration of pollutants is high, the energy efficiency of sewage treatment plants performs well. When the inflow load is high and the concentration of pollutants is low, the energy efficiency of sewage treatment plants is poor. Meanwhile, the energy consumption could be decreased by 11.61 %, 14 %, and 15.26 % respectively in different scenarios.

Removal of Carbon Dioxide and Hydrogen Sulfide from Natural Gas Using a Hybrid Solvent of Monoethanolamine and N-Methyl 2-Pyrrolidone.

The main goal of traditional methods for sweetening natural gas (NG) is to remove hydrogen sulfide (H2S) and significantly lower carbon dioxide (CO2). However, when NG processes are integrated into the carbon capture and storage (CCS) framework, there is potential for synergy between these two technologies. A steady-state model utilizing a hybrid solvent consisting of N-methyl-2-pyrrolidone (NMP) and monoethanolamine (MEA) has been developed to successfully anticipate the CO2 and H2S capture process from NG. The model was tested against important variables affecting process performance. This article specifically explores the impact of operational parameters such as lean amine temperature, absorber pressure, and amine flow rate on the concentrations of CO2 and H2S in the sweet gas and reboiler duty. The result shows that hybrid solvents (MEA + NMP) perform better in removing acid gases and reducing reboiler duty than conventional chemical solvent MEA. The primary purpose is to meet product requirements while consuming the least energy possible, which is in line with any process plant's efficiency goals.

Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

Effects of native species richness on reproduction of invasive Bidens pilosa vary with nutrient supply

BackgroundBoth increasing native species diversity and reducing nutrient availability can increase the ability of native plant communities to resist alien plant invasions. Furthermore, native species diversity and nutrient availability may interact to influence alien plant invasions. So far, however, little is known about the interactive effect of species diversity and nutrient availability on reproduction of alien invasive plants. We constructed native plant communities with one, four or eight species under low and high nutrient supply and then let them be invaded by the invasive alien plant Bidens pilosa.ResultsAt both high and low nutrient supply, increasing native species richness significantly increased aboveground biomass of the native plant community and decreased aboveground biomass and biomass proportion of the invader B. pilosa. Reproductive biomass of B. pilosa decreased significantly with increasing native species richness under high nutrient supply, but this effect was not observed under low nutrient supply. Net biodiversity effect on seed mass of B. pilosa decreased significantly with increasing native species diversity under high nutrient supply, but not under low nutrient supply. This was mainly because the selection effect became dominant with increasing species richness under high nutrient supply.ConclusionsOur study suggest that native species richness and nutrient supply can interact to influence reproduction of invasive alien plant species and that measures to help maintain a high level of native species richness and to reduce nutrient supply could be useful for efficient invasive plant control.

Operating Energy Needed for Desalination Systems in Cogeneration Plants

This study investigated the energy requirement for running desalination units coupled to cogeneration plants. Various cogeneration systems were explored using power- and heat-allocated approaches. The specific work and heat necessary for operating different desalination systems were determined. The investigation revealed that the specific work and heat remain consistent regardless of the desalination daily capacity. It was observed that the energy demand for operating a desalination system mainly relies on power plant efficiency. The investigation revealed that the energy demand for a plain multi-effect desalination system was lower than that for multi-effect desalination with thermal vapor compression. Additionally, the energy requirement for a multi-effect desalination system with preheaters was lower than that for plain multi-effect desalination. Comparisons also indicated that the energy demand of multi-stage flash exceeds that of different multi-effect desalination systems. Based on the primary thermal energy input, a universal performance ratio was used to evaluate the desalination unit performance. Furthermore, a new correlation was proposed to predict the universal performance ratio.

Efficiency and benchmarks for photovoltaic power generation amid uncertain conditions

Photovoltaic (PV) power generation systems are highly subject to weather and site conditions, thus, the construction of PV power plant projects must consider these uncertainties. This study analyzes the monthly electricity generation of 249 utility-scale PV power plants in Japan to evaluate their electricity generation efficiency. Applying the generic data envelopment analysis, benchmark values were identified for power generation from PV power plants. Furthermore, we implemented a Monte Carlo experiment to evaluate the impact of variability in solar irradiance and temperature on power generation efficiency. For our analysis, we considered three inputs—solar irradiance, temperature, and installed capacity—and electricity generation as the output. The results showed that inter-regional gap in the efficiency score between the west and north regions is 0.03, and this can be covered by a 0.1 increase in the DC/AC ratio. Additionally, variability in weather conditions affect both the efficiency of a power plant and production possibility frontier, in turn causing the benchmark values for a generic decision-making unit to vary. Increasing the generation capacity of power plants and operating them more efficiently is essential to expanding the use of renewable energy resources.

Efficiency improvement and flexibility enhancement by molten salt heat storage for integrated gasification chemical-looping combustion combined cycle under partial loads

Chemical-looping combustion offers a promising carbon capture technology for coal-fired power generation. Enhancing the operational flexibility and energy efficiency of such plants is crucial for achieving primary energy savings and renewable energy accommodation. In this study, the operating characteristics of an integrated gasification chemical-looping combustion combined cycle (IGCLCCC) under various loads are studied, and the deterioration mechanism of its thermodynamic performance is revealed. Further, an IGCLCCC scheme coupled with molten salt heat storage is proposed and evaluated from thermodynamic performance and operational flexibility. Study results indicate that the net efficiency of the IGCLCCC reaches 37.7% under 30% load, which is superior to those of conventional CO2 capture power plants under full load. Adopting the heat storage system significantly improves the operational flexibility and energy efficiency of the IGCLCCC by expanding its operating range from 30.0∼100.0% to 25.7∼105.3%. The equivalent round-trip efficiency of the heat storage system reaches 134.6% due to the upgrading of the thermal energy from the exhaust gas of the air turbine from original low levels to high levels driven by the exergy saving during the oxygen carrier-air reaction because of the air preheating to a high temperature.

Generic load regulation strategy for enhancing energy efficiency of chiller plants

In many chiller plants, high coefficient of performance (COP) is only achieved at a few favorable part load ratios (PLRs), while the COP is low at many other non-favorable PLRs. To address this issue, this study proposes a generic load regulation strategy that aims to maintain chiller plants operating at high COP, particularly under non-favorable PLRs. This is achieved by incorporating thermal energy storage (TES) units and timely optimizing the charging and discharging power of the integrated TES units. The optimal charging and discharging power is determined by solving a dynamic optimization problem, taking into account the performance constraints of the TES units and the chiller plants. To provide an overview of the energy-saving potential of the proposed strategy, a comprehensive analysis was conducted, considering factors such as building load profiles, COP/PLR curves of chillers, and attributes of the TES units. The analysis revealed that the proposed load regulation strategy has the potential to achieve energy savings ranging from 5.7% to 10.8% for chiller plants with poor COPs under unfavorable PLRs, particularly in buildings with significant load variations.

Evaluation of a grid-connected PV power plant: performance and agrivoltaic aspects

AbstractThe performance ratio, a globally recognized metric that correlates with reported global solar radiation values, serves as a crucial indicator for evaluating the efficiency of grid-connected PV plants. Also, a large scale PV power plant alone can afford some agricultural irrigation energy requirement of a region. In this study, the actual generation data from a power plant located in Bursa province in northwestern Türkiye, during its initial six years of operation have been analyzed. The analysis reveals that the annual electricity production of the power station reaches approximately 10 GWh. Notably, the time period between April and September witnesses a monthly electricity generation exceeding 1 GWh, with September emerging as the most productive month, characterized by an average performance ratio of 94.5% during this six-year period. However, over the span of six years, the highest average electricity generation occurs in July, peaking at 1.34 GWh. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%. From an environmental impact and global warming perspective, it is noteworthy that during the 36-month period in the summer season, the performance ratio exceeded 100% only three times. However, within the 32-month period in the winter season, the performance ratio exceeded 100% 19 times. This situation indicates that while the reported radiation rates by the managements are consistent with the actual values for the summer months, they need to be revised, especially for the winter months.

Planning of Production Capacities of Precast Construction Plants under Conditions of Changes in Demand for Products

The main criterion for a feasibility study of the production efficiency of precast construction plants, analysis of internal production reserves and optimal production volume is the production capacity indicator. Thus, the study of various Factors influencing the production capacity indicators is an important task. The results of research of the influence of the number of storeys and the set of block sections in the configuration of industrial residential buildings on changes in the ratio of product types in the range of the production programme of plants are presented in the paper. Fluctuations in the ratio of product types during the construction of different types of houses have been established. Based on the obtained results, the dependence of the production capacity plants on fluctuations in the ratio of product types has been constructed. It has been determined that the main reason for the revealed facts of a decrease in production capacity is the uneven loading of production lines with fluctuations in the ratio of product types in the range of production programs of precast construction plants. Taking into account the known facts of the dependence of production capacity on the concrete capacity of block sections of houses, the results of a comparison of the degree of influence of concrete capacity on the production capacity and the ratio of the main types of products in the range of production programs of plants are presented in the paper. It has been established that a key influence on the indicators of production capacity is exerted by changes in the ratio of product types. On the basis of the conducted research, optimal reserves of production capacity have been determined with a high share of different types of houses in the production programs of the plants. Taking into account the established indicators of production capacity reduction, their optimal reservation is within 20–25 % of the design capacity of the plants, which should serve as a reference point when planning the functioning of the production management system under the conditions of changes in demand for products.

Research of the regulator of an expander-generator unit

The relevance of research is determined by the tasks of increasing the efficiency of alternative and renewable energy power plants. The purpose of the work is to develop a method of coordinating the settings of the automatic excitation regulators and the speed of rotation of the rotor of the generator from the vortex installation of the expander-generator unit, which is part of the autonomous power consumption network. To achieve the goal, the methods of mathematical modelling in the MATLAB environment, physical modelling, processing of experimental data, theory of automatic control, theory of magnetic field, theory of electric machines, programming of microprocessor devices were used. Considering the need to expand the base of alternative energy sources, the task of controlling the energy plant for the utilization of excess pressure of gas transportation networks was considered. The study of the regulator of the expander-generator unit was based on a model reflecting the interaction of mechanical and electrical processes. A set of nonlinear differential equations corresponded to the description of these processes. Accurate consideration of nonlinearities of the considered control object complicates the implementation of the corresponding microprocessor control systems. Modelling is aimed at achieving a compromise between the required accuracy of object control and the possibilities of simplifying the object control structure. The use of experimental and datasheet data of the devices made it possible to evaluate the dynamics of turbine rotation and voltage generation, forming the factors for the untying of the control circuits. The use of MATLAB with the Lookup Table option made it possible to perform a multi-point linearization of the nonlinear description of the process, speeding up the modelling and adjustment of the regulator. This is important to incoordinate the settings of the frequency control loops and the output voltage, given the trade-off between the speed of the regulator and the minimization of oscillations in the generation frequency. Based on the obtained results, a regulator of the expander-generator unit was developed, which ensures the stabilization of the initial parameters of the unit under the conditions of electrical load disturbances. In practice, the obtained data can be used in the integration of renewable energy sources into the general energy system through the optimization of the operation of generators