Optimization Of Production Research Articles

Spatial and Temporal Variations of Soil pH in Farmland in Xinjiang, China over the Past Decade

Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of “higher in the south, lower in the north”. Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p < 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ.

Relationship between Self-Efficacy of Nurses and their General Wellbeing

Background: Nurses, as key personnel in healthcare delivery, play a critical role in the provision and coordination of care, prevention of adverse events, and optimization of health service productivity and patient outcomes. Self-efficacy is the belief, and confidence individuals feel in their ability to perform a particular task or action. Increased self-efficacy is the desired outcome of a self-management program. Several articles used Albert Bandura’s concept of self-efficacy. Objectives: To assess the self-efficacy and wellbeing of nurses. To find out relationship between self-efficacy and wellbeing of nurses. To find out relationship between self-efficacy and wellbeing with socio-demographic characteristics of nurse. Methodology: A descriptive, correlational study design. The study sample composed of 380 nurses who works in Al-Najaf Al-Ashraf hospitals and primary health care center. self-report questionnaire. The instrument used in this study different scales, self-efficacy scale consists of general and social=23item, and 5-well-being scale. Some nurses’ sociodemographic information was included as a separated part. The study was carried out from November 1st, 2023 up to 1 st of April 2024. Results: The majority of study sample (67.4%) are female, (60.8%) were with age less than 27 years old. (84.5%) of nurses are from urban residency. (46.3%) of them held diploma certificate, (89.5%) of nurses do not smoke, and not suffer from physical and mental illnesses. In addition, the overall assessment of nurses' well-being is normal. Conclusion: The majority of study sample were female (67.4); all the self-efficacy domains were high, and majority of nurses well-being are normal. The study shows that non-significant relationship between self-efficacy & wellbeing.

Technological and Nutritional Aspects of Bread Production: An Overview of Current Status and Future Challenges.

Bakery products, especially bread, exist in many homes worldwide. One of the main reasons for its high consumption is that the main raw material is wheat, a cereal that can adapt to a wide variety of soils and climates. However, the nutritional quality of this raw material decreases during its industrial processing, decreasing the value of fibers, proteins, and minerals. Therefore, bread has become a product of high interest to increase its nutritional value. Due to the high consumption of bread, this paper provides a general description of the physicochemical and rheological changes of the dough, as well as the sensory properties of bread by incorporating alternative flours such as beans, lentils, and soy (among others). The reviewed data show that alternative flours can improve fiber, macro, and micronutrient content. The high fiber content reduces the quality of the texture of the products. However, new processing steps or cooking protocols, namely flour proportions, temperature, cooking, and fermentation time, can allow adjusting production variables and optimization to potentially overcome the decrease in sensory quality and preserve consumer acceptance.

Optimization of enzymatic production of anti-skin aging biopeptides from white sorghum [Sorghum bicolor (L) Moench] grain

Recently, kafirins from white sorghum [Sorghum bicolor (L) Moench] grain have shown promise as a source of biopeptides with anti-skin aging effects (anti-inflammatory, antioxidant, and inhibition of photoaging-associated enzymes). This study employed response surface methodology (RSM) to optimize the extraction and enzymatic hydrolysis of kafirins (KAF) for the production of peptides with anti-skin aging properties. The optimization of conditions (reaction time and enzyme/substrate ratio) for liquefaction with α-amylase and hydrolysis of KAF with alcalase was performed using 32 complete factorial designs. Subsequently, ultrafiltered peptide extracts were obtained with molecular weights of 1–3 kDa (KAF-UF3) and lower than 1 kDa (KAF-UF1), which mainly contain hydrophobic amino acids (proline, leucine, isoleucine, phenylalanine, and valine) and peptide fractions with molecular weights of 0.69, 1.14, and 1.87 kDa. Consequently, the peptide extracts protected immortalized human keratinocytes (HaCaT cells) from ultraviolet B radiation (UVB)-induced damage by preventing the decrease and/or restoring the activity of antioxidant enzymes [superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px)]. Furthermore, KAF-UF3 and KAF-UF1 inhibited (20–29%) elastase and collagenase overactivity in UVB-exposed murine fibroblasts (3T3 cells). Thus, KAF-UF3 and KAF-UF1 exhibited behavior similar to that observed with glutathione (GSH), suggesting their potential as functional peptide ingredients in skincare products.

Qualitative Perturbation Analysis and Machine Learning: Elucidating Bacterial Optimization of Tryptophan Production

Qualitative Perturbation Analysis and Machine Learning: Elucidating Bacterial Optimization of Tryptophan Production

Using Big Data and analytics in accounting for strategic decisions

The purpose of this paper is to study the potential of using Big Data and analytics in accounting in order to improve strategic decision-making processes at the enterprise. To achieve the goal, the following tasks were performed in the research process: the concepts, advantages and disadvantages of using Big Data and analytics in accounting were considered; directions for using Big Data in accounting in general and for improving strategic decision-making processes in particular were investigated. The use of advanced technologies in accounting can significantly improve the quality and relevance of accounting information, reduce preparation time and ensure availability of data in real time. Studying the basic principles of accounting organization using information technologies, efficiency, accuracy, security and compliance with regulatory requirements were identified as key. The potential impact of Big Data on the accounting profession and its practices is very significant. As the volume of available information grows, new opportunities appear to improve the accuracy and efficiency of traditional accounting approaches. Big Data analysis can reveal financial trends, patterns, and anomalies that were previously difficult to access or invisible through traditional methods. The use of Big Data and analytics in accounting for making strategic decisions can significantly contribute to the achievement of the goals specified in the modern economic paradigm. Simplified acquisition and analysis of large volumes of data makes it possible not only to increase the accuracy and speed of decision-making in financial management, but also to respond more effectively to changes in macroeconomic conditions, such as diversification of production and exports, improvement of living conditions of the population, and reduction of social inequality. The introduction of high technologies, including Big Data analysis, is becoming a key tool for ensuring sustainable economic development, contributing to the growth of productive forces and optimization of production processes in accordance with modern requirements and challenges of the global market.

Comprehensive exploration of riboflavin solid forms and preparation mechanism of stable forms

Riboflavin is a water-soluble vitamin regarded as an essential nutrient participating in biological oxidation in vivo. The strong hygroscopicity and crystalline phase impurity of riboflavin in the solid state are obstacles to its industrial production and storage. Riboflavin is polymorphic and is usually produced and marketed in crystalline form. However, the phase transformation among riboflavin polymorphs and preparation mechanism of stable crystal form have not been clarified, hindering the production optimization and storage management of products. In this work, the relationship of phase transformation among the riboflavin polymorphs was elucidated, and their thermal stability as well as hygroscopicity were evaluated. Anhydrate I is the most stable form with the best thermal stability and lowest hygroscopicity could not be prepared in antisolvent crystallization process, where monohydrate is the most frequent form. Consequently, a solution-mediated phase transformation (SMPT) process was proposed to prepare anhydrate I from monohydrate. In-situ Raman, UV–VIS spectrum and Powder X-ray Diffraction were utilized to monitoring the SMPT process. It was found that the nucleation and growth of anhydrate I was the rate-determining step. Elevated temperatures, reduced solid loads and addition of seeds can accelerate the conversion procedure. This work provides guiding significance for the product development and quality control of Riboflavin.

Variability of strength and stiffness assessment for non-profile beam structures

Introduction. Traditional ways to increase the structural strength and stiffness of beams are now almost exhausted, and optimization of production and operation technologies is mostly based on the use of new materials and increasing their reliability as bearing elements of a complex geometric profile. Bearing elements of building structures operate under high loads and, even despite their predominantly static nature, experience a complex volumetric stress and strain state, which can scarcely ever be confirmed empirically and statistically.Aim. To propose an approach to assessing the strength and stiffness of beam structures operating under conditions of transverse bending of a complex geometric profile.Materials and methods. Classical energy methods, including Castigliano, Maxwell–Mohr and Vereshchagin methods (moment area method), have been used to assess the strength and stiffness of non-profile beam structures, initial parameters, differential equations of the deflection curve.Results. The similarity parameters (fitting criteria) of Mises–Hencky and Zhurkov reflect the behavior of a brittle or plastic beam material reasonably well. These characteristics are proposed to be combined in the Arrhenius–Zhurkov durability functions.Conclusions. The dependencies given in the calculations can be recommended for improving the flexural static and fatigue strength, as well as the stiffness of the bearing elements of building structures.

A dual-porosity flow-net model for simulating water-flooding in low-permeability fractured reservoirs

The physics-based data-driven flow-network models with high computational efficiency have received great attention as the promising surrogate models for reservoir numerical simulation. However, the existing flow-network models developed for water-flooding reservoirs fail to consider different seepage characteristics of matrix and fractures and cannot be straightly applied to simulate the water-flooding process in low-permeability fractured reservoirs. In this study, we combine the flow-network model with the dual-porosity model to propose a new physics-based data-driven surrogate model, namely the Dual-Porosity Flow-Network Model (Flow-Net-DP), which can consider the non-Darcy flow in tight matrix and the stress-sensitive effects and preferential flow characteristic in fractures. Specifically, we refer to the dual-porosity model and use two channels to represent the connections between wells: one indicates the fracture system, the other represents the matrix system, and the fluid exchange between these two systems is considered by using a transfer function. Besides, each channel is discretized into one-dimensional grids, and a fully implicit scheme with Newton iteration is used to calculate pressure and saturation. Moreover, we establish an automated history matching method by using the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm to calibrate model parameters of Flow-Net-DP, and develop a production optimization method by using the Differential Evolution (DE) algorithm. Finally, the numerical simulation, history matching, and production optimization are conducted on different numerical examples to validate the capability of Flow-Net-DP. The results indicate that the Flow-Net-DP can provide a better description of water flooding process in low-permeability fractured reservoirs compared with the existing flow-network model, especially the rapid water breakthrough caused by the preferential flow characteristic in fractures. Furthermore, both history matching and production optimization based on the Flow-Net-DP yield satisfactory outcomes. For instance, when the optimization results are similar to the full-order simulation model, the optimization speed of Flow-Net-DP is increased by more than five times.

Coordinated scheduling of wind-solar-hydrogen-battery storage system for techno-economic-environmental optimization of hydrogen production

Green hydrogen production powered by renewable energy emerges as a promising alternative to reduce emissions in the context of the global Net Zero target. Nevertheless, the inherent randomness and intermittency of renewables such as wind and solar cause prominent fluctuations in power supply to water electrolyzers, which pose challenges to their operational stability and efficiency, leading to decreased performance and shortened lifespan. To address these challenges, coordinated operation scheduling of the renewables-hydrogen system with multi-electrolyzers is investigated to enhance the system’s stability and efficiency while contributing to higher cost-effectiveness and sustainability. Meanwhile, the battery storage system is incorporated as a buffer to mitigate the variability of renewable power output and as a supplement to alkaline electrolyzers (AEL). This study also designs a dung beetle optimizer-gated recurrent unit (DBO-GRU) model for wind-solar power forecasting, offering guidance for more efficient and adaptive scheduling of AEL operations. To achieve multi-optimized scheduling of this integrated energy system, a refined rolling optimization strategy is developed, considering technical, economic, and environmental benefits to comprehensively improve green hydrogen production. Case studies using real-world scenarios validate its superiority across multiple time scales. For instance, compared with two other scheduling methods, the results show that the proposed strategy can respectively increase hydrogen production, reduce start-stop cycles, decrease LCOH, and lower carbon emissions by up to 4.19%, 25.40%, 7.98%, and 5.71% over a yearly-long operation. Therefore, the refined rolling optimization strategy simultaneously improves the overall system efficiency, operational cost-effectiveness, and sustainability of hydrogen production, underscoring its advantages and significance.

Crop Recommendation and Crop Yield Estimation Using Machine Learning and IoT

In the contemporary landscape of agriculture, the amalgamation of cutting-edge technologies such as machine learning (ML) and Internet of Things (IoT) has emerged as a transformative force, revolutionizing traditional farming practices. This paper introduces a comprehensive framework for crop recommendation and yield estimation, leveraging the power of ML algorithms and IoT devices to optimize agricultural operations. The dataset utilized in this study was meticulously curated from reputable sources including Kaggle and Google, encompassing a vast array of agricultural parameters ranging from soil characteristics to climate conditions. This rich dataset serves as the foundation for training and validating ML models, enabling robust analysis and prediction. A diverse range of ML algorithms was employed to process and interpret the dataset, with the objective of identifying the most proficient algorithm for crop recommendation and yield estimation tasks. Through rigorous experimentation and comparative analysis, the algorithm exhibiting superior performance across multiple evaluation metrics was meticulously chosen. Moreover, to bridge the gap between sophisticated ML techniques and practical application in the field, a user-friendly interface was developed using Flask, a micro web framework for Python. This intuitive interface empowers stakeholders to seamlessly access the trained model, facilitating real-time crop recommendations and yield estimations based on input parameters. The culmination of this research endeavor presents a paradigm shift in agricultural management, offering stakeholders unprecedented insights into crop selection and productivity optimization. By harnessing the potential of ML and IoT technologies, this framework not only enhances decision-making processes but also contributes to sustainable agricultural practices and global food security.

Development of an HSV-1 production process involving serum-reduced culturing and bead-to-bead transfer

Herpes simplex virus type 1 (HSV-1) plays an important role in the field of gene therapy and viral vaccines, especially as an oncolytic virus. However, the mass production of HSV-1 viral vectors remains a challenge in the industry. In this study, a microcarrier-mediated serum-reduced medium culture was used to improve the bioprocess of HSV-1 production and increase HSV-1 yields. The composition of the culture media, which included a basal medium, serum concentration, and glutamine additive, was optimized. The process was successfully conducted in a 1 L bioreactor, and virus production was threefold greater than that of conventional processes with a 10% serum medium. The bead-to-bead transfer process was also developed to further increase scalability. In spinner flasks, the detachment rate increased from 49.4 to 80.6% when combined agitation was performed during digestion; the overall recovery proportion increased from 37.9 to 71.1% after the operational steps were optimized. Specifically, microcarrier loss was reduced during aspiration and transfer, and microcarriers and detached cells were separated with filters. Comparable cell growth was achieved with the baseline process using 2D culture as the inoculum by exchanging the subculture medium. To increase virus production after bead-to-bead transfer, critical parameters, including shear stress during digestion, TrypLE and EDTA concentrations in the subculture, and the CCI, were identified from 47 parameters via correlation analysis and principal component analysis. The optimized bead-to-bead transfer process achieved an average of 90.4% overall recovery and comparable virus production compared to that of the baseline process. This study is the first to report the optimization of HSV-1 production in Vero cells cultured on microcarriers in serum-reduced medium after bead-to-bead transfer.Key points• An HSV-1 production process was developed that involves culturing in serum-reduced medium, and this process achieved threefold greater virus production than that of traditional processes.• An indirect bead-to-bead transfer process was developed with over 90% recovery yield in bioreactors.• HSV-1 production after bead-to-bead transfer was optimized and was comparable to that achieved with 2D culture as inoculum.

Hyper β-glucosidase producer Beauveria bassiana SAN01—optimization of fermentation conditions and evaluation of saccharification potential

AbstractThe hyper-production of β-glucosidase by a local strain of Beauveria bassiana under submerged conditions is reported in this study. The initial screening of seven agricultural residues showed that the haulm of Bambara—an underutilized African legume—supported the highest β-glucosidase production; hence, statistical optimization of enzyme production was done using this biomass as the sole carbon source. Plackett–Burman design identified the concentrations of Bambara haulm, KCl, and NaCl as well as agitation speed and incubation time as the most significant factors affecting enzyme production. Subsequently, the central composite design predicted the optimal conditions (Bambara 57 g/L, KCl 302 mg/L, NaCl 154 mg/L, agitation speed 150 rpm, and incubation 223 h) for B. bassiana β-glucosidase production, which were further validated. The generated quadratic model was deemed significant judging from its F-value (201.63), adequate precision ratio (45.74), as well as the R2 (0.9988), adjusted R2 (0.9938), and predicted R2 (0.9195) values. The optimization resulted in a ~5.36-fold increase in enzyme levels from the unoptimized production of ~133 to 711 U/mL. The enzyme was also demonstrated to efficiently hydrolyze cellobiose, converting more than 90% of the substrate to glucose. These results further establish the resourcefulness of the B. bassiana strain for the production of β-glucosidase enzyme, having immense potential, especially in the food and energy industries.

OPTIMIZATION OF MILK PROCESSING PROCESSES AND ANALYSIS OF OBTAINED SOLUTIONS

This study focuses on the analysis of optimization and sensitivity in dairy production, utilizing linear programming to achieve an optimal production structure and maximize net income. The model identifies key products with the highest revenues and those having the greatest impact on overall revenue and costs. Sensitivity analysis reveals which products are most sensitive to price changes and how these changes affect production structures, providing valuable insights for dairy management in pricing strategies and future investments. Additionally, the research opens opportunities for further improvements and exploring new market avenues and product diversification in the dairy industry.

Comparative Evaluation of the Application Effectiveness of Intelligent Production Optimization Methods in Offshore Oil Reservoirs

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization.

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

Methodological aspects of management of sustainable development of agricultural industrial and rural territories: measurement, planning, forecast

The article deals with the change in the “paradigm” of the agricultural economy that occurred and was recorded in the “Concept for the Development of Rural Territories of the Russian Federation” in 2010 (developed in the “Strategy for Sustainable Development of Rural Areas of the Russian Federation for the period up to 2030”, 2015 [1]), but is not widely recognized (according to T. Kuhn’s theory of scientific revolutions), which is reflected in the increasing complexity of the systems studied by scientific economists -agrarians: agriculture (mechanistic model and independent controllable variables) — agro-industrial complex (intelligent observable system, the possibility of implementing management and quality control) — rural areas (“multi-intelligent observing systems” — social model, interdependent variables). Therefore, today it is impossible to reduce all agroeconomic research to “maximizing profit per worker or per unit of area”. Based on the need to apply a systematic approach, in conditions of challenges and sanctions, the need to expand the capabilities of traditional cost assessments based on the use of indicators (natural and relative assessments) of the quality of management decision-making is substantiated. When developing methods of analysis and management (agriculture, agro-industrial complex, rural areas), it should be taken into account that the systems approach updated in the work has also undergone three transformations over 80 years: operations research (optimization of agricultural production) or cybernetics 1.0; cybernetics 2.0 and open systems (quality management); cyber-physical systems in agriculture and agro-industrial complex.

Impact of well completion types on oil drainage area in reservoirs

Well completion design plays a crucial role in oil production and reservoir management. This study examines the influence of various well completion types on the oil drainage area within reservoirs, with the goal of enhancing our understanding of how completion methods affect hydrocarbon recovery. Using advanced reservoir engineering techniques and simulations, this research investigates the dynamic interaction between well completion and the accessibility of oil resources. The study provides a comprehensive analysis of different well completion types, including open-hole completions, cased-hole completions, and multilateral completions, and evaluates their collective impact on the drainage area. It also explores the interplay between well completion strategies and reservoir properties, such as permeability, lithology, and fluid characteristics. The findings of this study reveal that the choice of well completion type can significantly influence the drainage area by affecting the flow paths and reservoir contact. Different completion methods can extend or limit the reach of hydrocarbon recovery, impacting oil production rates. The research underscores the importance of optimizing well completion design to maximize reservoir performance. Furthermore, the study addresses operational considerations related to well completion, including equipment selection, production optimization, and workover requirements. It offers insights into the economic and operational implications of different well completion strategies, providing valuable guidance for reservoir management. The outcomes of this research contribute to a deeper understanding of how well completion types impact the oil drainage area in reservoirs. This knowledge is valuable for reservoir engineers, operators, and industry professionals involved in oil production, assisting them in making informed decisions to optimize oil recovery, reservoir performance, and well completion strategies.

The Importance of GRP Composite Material in Liquid Fertilizer Tanks and Production Optimization of the Tanks

Kompozit malzemeler sahip oldukları üstün özelliklerden dolayı imalat sektöründe birçok alanda oldukça yaygın kullanılmaktadır. Bu çalışmada cam elyaf takviyeli plastik (CPT) kompozit malzemeden tarım sektöründe sıvı gübrelerin taşınmasında kullanılan tanker imalatı ve optimizasyonu çalışılmıştır. Özellikle bitkilerin gelişiminde organik gübre kullanımına önem verilmektedirler. Gübre içeriğinde bulunan azot, fosfor, potasyum, kalsiyum, magnezyum, kükürt, demir, manganez, bakır, çinko ve bor gibi büyük miktarlardaki elementler taşıma kapları ile kimyasal reaksiyona girebilir. Bu kimyasal reaksiyonları gözlemlemek amacı ile deney kapları hazırlanmıştır. Bu kaplarda yoğunlaştırılmış farklı sıvı gübre çeşitleri oluşturulmuştur. Bunlar için uygun zaman aralıkları seçilerek pH dereceleri masa tipi “ohaus AB23PH-F” ölçülmüştür. Hazırlanan tavuk gübresinin pH değeri 8,44 küçükbaş hayvan gübresinin pH değeri 8,34 güvercin gübresinin pH değeri 7,54 ve büyükbaş hayvan gübresinin pH değeri 9,03 olarak ölçülmüştür. Bununla birlikte sıvı gübreler 20 °C de kapalı yalıtımlı kap içerisinde bekletilip nem oranları %58 olarak belirlenmiştir. Deney kaplarında sıvı gübre içinde bekletilen ST37 çelik numuneler ve CTP nin SEM, XRD ve optik görüntüleri ve kütle kayıpları değerlendirilmiştir. Korozif ortamlardaki CTP numunelerinde korozyona rastlanmamıştır. İki faklı malzemeden 10m3 hacminde tanker tasarımı yapılmıştır. Sonlu elemanlar analizi sonucunda güvenli olarak kullanılabilecek CPT tankerin et kalınlığı 12mm bu tankerin ağırlığı 702 kg olarak hesaplanmıştır. Aynı hacim ve 3 mm kalınlıkta tasarlanan ST37 çelik tankerin ağırlığı 3504 kg olmaktadır. Korozyona karşı dirençli ve yaklaşık 5 kat daha hafif olması CPT kompozit malzemeden imal edilen tankerlerin uygun olacağı düşünülmüştür.

ANAEROBIC CO-DIGESTION OF DIFFERENT MIXTURES OF ORGANIC WASTES – OUR EXPERIENCE

Anaerobic digestion is a biotechnological method of biogas generation from organic wastes. Anaerobic co-digestion can be considered as the instantaneous anaerobic digestion of two or more substrate and co-substrate mixtures. Anaerobic co-digestion is a pragmatic method to resolve the difficulties related to substrate properties and system optimization in single-substrate anaerobic digestion. During the last two decades our multidisciplinary team performed a lot of experiments of anaerobic co-digestion of different organic wastes activated sludge, cattle manure, swine manure, milk whey, wasted fruits and vegetables etc. Different ratios of mixtures of these organic wastes, in binary and triple mixtures, have been investigated in view to maximize the obtained energy (methane) production. The time for reaching of the process steady state in laboratory anaerobic digesters have been determined to be ten days’ period. Results obtained have shown that some binary mixtures are more perspective in regards to biogas production optimization. It has been found that the addition of 3rd substrate to some binary mixtures could lead to decrease of bioprocess effectiveness.