Levels Of Phosphorus Concentrations Research Articles

Phosphorous concentration in iron-rich rocks of the Chilpi Group, Bastar Craton, India: implications on late Palaeoproterozoic seawater palaeo-productivity

AbstractThe concentration of the bio-limiting nutrient element, phosphorus (P), in seawater is important for primary marine productivity and the evolution of life on geological time scales. The molar percentage of P/Fe in banded iron formations (BIF) and iron oxide-rich chemical sediments is a good proxy for the first-order approximation of seawater P concentration. Bio-available concentration of phosphorus in Precambrian, especially during the late Palaeoproterozoic Era (2.0–1.8 Ga), is poorly constrained. We evaluated the P/Fe ratios of iron-rich rocks from the late Palaeoproterozoic Chilpi Group, Bastar Craton, Central India. The bulk rock molar percentage of P/Fe ratios of the Chilpi rocks vary between 0.11 and 1.17 (average 0.51 ± 0.3), and the average of EPMA spot analysis P/Fe molar ratio is 0.32 ± 0.4; both have values similar to Archaean BIFs of the world. The observed low molar ratio is not an artefact of contamination from terrestrial sources, diagenetic alterations or high-temperature hydrothermal inputs; it indicates the deposition from phosphorus-lean seawater. The modelled P/Fe molar ratio in the Chilpi Group suggests that the concentration of phosphorus in the shallow marine environment was less than 0.12 μM. The low level of phosphorus concentration in seawater during the late Palaeoproterozoic Era is interpreted to be a consequence of the low primary production during a period of low atmospheric oxygen content, which might have impeded the evolution of eukaryotes.

The Influence of Phosphate-Ester-Based Additives on Metal Cutting Fluid Behavior during the Machining of Titanium Alloy

The behavior of four phosphate ester additives with varying levels of phosphorus concentrations (very high, high, medium, and low) was examined through the course of drilling a Ti-6Al-4V titanium alloy at a constant metal removal rate (4.2 mm3/s). Cutting fluid (CF) additives were evaluated using torque, specific cutting energy (SEC), and tool wear. The drilling conditions employed had a significant influence on the performance of the phosphate ester additives. At 0.105 m/s and 0.188 m/s, the phosphate ester with very high phosphorous levels possessed the lowest SCE and torque values. The high-phosphorous-level phosphate ester displayed enhanced drilling performance at 0.293 m/s. At 0.419 m/s, the SCE and torque performance of the medium-phosphorous-level phosphate ester was preferable. The drilling performance of the phosphorus esters was observed to be related to the working mechanisms of the additives, which, in turn, was associated with the formation of a phosphorus-rich tribolayer and an organophosphate tribolayer on the cutting blade.

Pollutant Removal Efficiency of a Bioretention Cell with Enhanced Dephosphorization

Low impact development can contribute to Sustainable Development Goals (SDGs) 2, 6, 7, 11, and 13, and bioretention cells are commonly used to reduce nonpoint source pollution. However, although bioretention is effective in reducing ammonia nitrogen and chemical oxygen demand (COD) pollution, it performs poorly in phosphorus removal. In this study, a new type of enhanced dephosphorization bioretention cell (EBC) was developed; it removes nitrogen and COD efficiently but also provides excellent phosphorus removal performance. An EBC (length: 45 m; width: 15 m) and a traditional bioretention cell (TBC) of the same size were constructed in Anhui, China, to treat rural nonpoint source pollution with high phosphorus concentration levels. After almost 2 years of on-site operation, the ammonium nitrogen removal performance of the TBC was 81%, whereas that of the EBC was 78%. The COD removal rates of the TBC and EBC were 51% and 65%, and they removed 51% and 92% of the total phosphorus, respectively. These results indicate that the TBC and EBC have similar performance in the removal of ammonium nitrogen and COD, but the EBC significantly outperforms the TBC in terms of total phosphorus removed.

Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression

The existence of contaminants in metal alloys products is the main problem affecting the product quality, which is an important requirement for competitiveness in industries. This paper proposes the application of a relevance vector machine for regression (RVR) and a support vector machine for regression (SVR) optimized by a self-adaptive differential evolution algorithm for regression to model the phosphorus concentration levels in a steelmaking process based on actual data. In general, the appropriate choice of learning hyperparameters is a crucial step in obtaining a well-tuned RVM and SVM. To address this issue, we apply a self-adaptive differential evolution algorithm, which is an evolutionary algorithm for global optimization. We compare the performance of the RVR and SVR models with the ridge regression, multiple linear regression, model trees, artificial neural network, and random vector functional link neural network models. RVR and SVR models have smaller RMSE values and better performance than the other models. Our study indicates that the RVR and SVR models are adequate tools for predicting the phosphorus concentration levels in the steelmaking process.

Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process

In the past decade, relevance vector machines have gained the attention of many researchers, and this machine learning technique is a Bayesian sparse kernel method, both for classification and regression problems. In general, the choice of appropriate learning hyperparameters is a crucial step in obtaining a well-tuned model. To overcome this issue, we apply a self-adaptive differential evolution algorithm. In this paper, we propose a relevance vector machine for regression combined with a novel self-adaptive differential evolution approach for predictive modelling of phosphorus concentration levels in a steelmaking process with real data. We compared the performance of proposed relevance vector machine (RVM) with other machine learning techniques, such as random forest (RF), artificial neural network (ANN), K-nearest neighbors (K-NN), and also with statistical learning techniques as, Beta regression model and multiple linear regression model. The RVM has performance better than RF, ANN, K-NN, and statistical techniques used. Our study indicates that RVM models are an adequate tool for the prediction of the phosphorus concentration levels in the steelmaking process.

Forecast model for dephosphorization process of ferromanganese steels using artificial neural networks

One of the main problems affecting the quality of steel products is the existence of contaminants in alloy steel, being phosphorus (P) a major contamination element interfering with the steelmaking process. The increased P concentration levels can severely affect physical integrity of steel bonds, thus threatening the quality of the final product. The dephosphorization process of Ferromanganese consists by carbothermic reaction that involves the control of the manganese volatilization and reduction of manganese oxide in injection of oxygen. Therefore, we propose to forecast model for dephosphorization process of Ferromanganese steels in a steelmaker industry, that allows estimating the phosphorus concentration levels at the final refining process. We chose the artificial neural network models because it is computational models inspired in the human nervous system and an architecture of neural network with the Levenberg-Marquadt algorithm and Kolmogorov theorem for improving the estimation technique. The developed model presented excellent performance with a percentage error of 0.09%. Based on this created estimation model it is possible to estimate the impact of certain P concentration levels in FeMnMC beforehand, with a considerable amount of reliability. Keywords: Ferromanganese alloys, dephosphorization process, Neural networks, Kolmogorov theorem. 1

Forecasting Modeling for Energetic Efficiency in an Industrial Process

The quality of products and processes is a permanent challenge for industries, and such challenge is no different in steelmaking processes. One of the main problems affecting the quality of steel products is the existence of contaminants in alloy steel, and phosphorus (P) is a major contamination element interfering with the steelmaking process. The increased P levels can severely affect the physical integrity of steel bonds, thus threatening the quality of the final product. This paper proposes a robust approach to model the phosphorus concentration levels in the steelmaking process. The proposed approach consists in applying the artificial neural networks techniques for improving the energetic efficiency of the industrial process. We used the improved neural network models inspired in the human nervous system for processing the information. The different techniques used for modelling the phosphorus levels investigate the variables that have a significant influence on refining process. Based on the better predictive model, the increase of phosphorus levels in the final product is related to initial levels of carbon, oxygen, magnesium, manganese oxide and calcium oxide. The results illustrate the efficiency of the techniques used in the modelling, with emphasis on the adequacy of the predictive models constraints in the refining process. This study presents a relevant strategy to model characteristics’ of raw material based on forecasting strategy to the efficiency of alloys and steel industry.

Modeling of <i>Anabaena flos-aquae</i> growth kinetics of light intensity and temperature within different levels of phosphorus concentrations

Anabaena flos-aquae is one of the common harmful cyanobacteria species. However,its growth kinetics co-impacted by phosphate concentration,light intensity and temperature have rarely been reported. In this paper,eight PO3-4-P concentration gradients( 0,0. 1,0. 2,0. 5,0. 75,1. 0,2. 5 and 5. 0 μmol / L),four water temperatures( 15,20,25 and 30℃) and four light intensities( 1000,2000,3000 and 5000 lx) were selected to carry out single factor batch experiments. A maximum specific growth rate( μmax) was used to indicate algae specific growth rate under optimal growth condition. Half-saturation constant( KS)could be applied to describe affinity of algae to nutrients. Regression results from Monod model showed that Anabaena flos-aquae reached μmaxat 20℃ and 3000 lx. The μmaxand KSwere 0. 447 d-1and 0. 081 μmol P / L,respectively. Regression results based on Lehman and Steele Model that examines the relationship between specific growth rate and continuous changing of temperature as well as light intensity,demonstrated that the optimum light intensity( Ik) and temperature( Topt) were 2650. 93 ± 88. 19 lx and21. 22 ± 0. 98℃,respectively. Besides,the kinetic parameters μmax,KS,and Toptfrom Lehman Model were 0. 421 ± 0. 011 d-1,0. 055 ± 0. 009 μmol P / L and 21. 22 ± 0. 98℃,respectively. In the Steele model,μmax,KSand Ikwere 0. 461 ± 0. 010 d-1,0. 051 ± 0. 009 μmol P / L and 2650. 93 ± 88. 19 lx,respectively.

인 결핍에 따른 하수배양 미세조류의 지방산 특성 분석 연구

하수에서 배양된 미세조류인 Chlorella vulgaris AG10032, Ankistrodesmus gracilis SAG278-2, Scenedesmus quadricauda AG10308은 오폐수에서 질소 및 인 제거가 우수하고 높은 지질을 함유하고 있어서, 오폐수에서 바이오연료나 기타 바이오 기반의 자원 회수에 유용한 생물자원이다. 오폐수 환경에는 다양한 인의 농도가 존재하는데, 인의 농도 특히 인 결핍조건에 따른 조류의 지방산 특성에 대한 정보가 매우 제한적이다. 이는 표준 분석방법이 정립되어 있지 않은데 일부 기인한다. 본 연구에서는 미생물의 지방산을 분석하는데 일반적으로 사용되는 무극성 컬럼법과 식물성 지질을 분석하는데 널리 사용되는 극성 WAX-type 컬럼 GC-FID 방법의 미세조류 지방산 분석 성능을 비교 분석하였고, 하수배양 조류에 적용이 보다 적절한 방법을 이용해서 인 결핍에 의한 조류 지방산 생성특성에 미치는 영향을 평가하였다. 그 결과 무극성 컬럼 방법에 비해 극성 WAX-type 컬럼 GC 방법이 <TEX>$C_{18:3}$</TEX> 지방산과 같은 고불포화지방산을 규명하는 분석능력이 우수하였고, 실제 하수배양에서 배양 분리된 미세조류의 지방산 분석에 보다 정확한 결과를 보이었다. 이 WAX-type 컬럼 방법으로 인 결핍 영향을 분석한 결과, 미세조류의 지방산 조성과 생성량은 인의 농도 변화에 크게 영향을 받지 않음을 밝히었다. Wastewater-adapted microalgae such as Chlorella vulgaris AG10032, Ankistrodesmus gracilis SAG278-2 and Scenedesmus quadricauda AG10308 are useful biological resources for recovering biofuel and other bio-based materials from wastewater because of their efficient removals of nitrogen and phosphorus from wastewater and their high fatty acid contents in biomass. Although the concentrations of phosphorus typically vary in wastewater environment, very little is known about the effect of phosphorus concentration, especially phosphorus starvation, on microalgal fatty acid synthesis. This is partially due to the lack of methodological establishment for algal fatty acid analysis. In this study, we compared the analysis performances of microalgal fatty acids by two different methods; one is a non-polar GC (gas chromatography) column based method, which is generally used for microbial fatty acids, and the other is a polar WAX-type GC column method, which is typically used for plant fatty acids. And then, we explored the effect of phosphorus concentration levels on fatty acid production in microalgae cultivated from wastewater. As results, the polar WAX-type column method has better ability to separate poly unsaturated fatty acids (PUFA) including <TEX>$C_{18:3}$</TEX> (linolenic acid), and was found to be more applicable in analyzing fatty acids from wastewater-cultivated microalgae than the non-polar column method. The fatty acid characterization by the WAX-type column method revealed little effect of phosphorus starvation on the quantity and composition of fatty acids from wastewater-cultivated microalgae.

The Pollution Load by Nitrogen and Phosphorus in the Cetina River

The objective of the investigations of the Cetina River, located in southern Croatia, was to record specific characteristics and properties of the Cetina waters at nine stations. In addition to measurements undertaken in the Cetina River, the water quality of its most significant springs and tributaries, such as Kosinac, Silovka, Studenci and Mala Ruda, Velika Ruda and Grab, has also been measured. The water quality in the Cetina watershed has been evaluated in the following storage reservoirs: Peruca, Busko Blato and Prancevici. The nitrogen compounds and phosphorus concentrations have been estimated at all these sampling sites over a 3-year period (2005–2008). Concentration levels at the Cetina-Vinalic sampling site for total N (from August 2005 to December 2008) ranged from 0 to 1.759 mg/L, for NH3–N from 0 to 0.374 mg/L, for NO3–N from 0.063 to 0.916 mg/L and for PO4–P from 0 to 0.099 mg/L. The results prove that the Cetina-Vinalic sampling site is not polluted by nitrogen and phosphorus compounds. The river section from Trilj to the Prancevici dam, where the water is used for the water supply of Omis, Makarska and Dalmatian islands, has been polluted by wastewater because the majority of agricultural area, roads, industry and settlements are located upstream of it. The highest concentration for total N of 1.128 mg/L and of 1,527 total coliforms in 100 mL, expressed as a mean value for a 3-year period of investigations, was found at the sampling site Trilj. The results of concentration changes at the Cikotina Lađa and Cetina Radmanove Mlinice sampling sites show no regularities. The highest concentration for total N of 0.941 mg/L was measured at the Cetina Radmanove Mlinice during 2007. The highest concentration for NO3–N of 0.916 mg/L was measured at the same sampling site. According to the investigations of the water quality of the Cetina springs and tributaries, the bacteriological most polluted river spring is Kosinac, and the bacteriological most polluted river tributary is Grab. With reference to the water quality in the Cetina storage reservoirs, it may be concluded that the lowest quality standard has been found within the Prancevici storage reservoir regarding nitrogen compounds and phosphorus concentration levels.

The suitable phosphorus concentration level for restoration by duckweed

The suitable level of phosphorus concentration in waterbody was determined for the growth of duckweed. Lemna minor and Spirodela polyrhiza were cultured in medium containing five different phosphorus concentrations (0.01, 0.2, 0.5, 5 and 50 mg l−1). The increase of frond number, fresh weight, soluble protein, root length, peroxidase, catalase and malondialdehyde were measured. The two species of duckweed can survive under all circumstances. They grew better when phosphorus concentrations were 0.5-5 mg l−1 than other phosphorus concentrations. This study indicated that the two species of duckweed are suitable for recovering the eutrophicated natural waterbodies in China.

Economic Analysis of an Aquaponic System for the Integrated Production of Rainbow Trout and Plants

Conventional treatment alternatives for phosphorus in wastewater, whether they employ chemical precipitation, physical removal, or land application technologies, represent a significant additional cost to the owner of an aquaculture operation. Plant-based removal of nutrients has the potential to generate additional revenues, which can offset treatment costs. The objective of this analysis was to describe the economic relationship between a 22,680 kg per year recirculating rainbow trout (Oncorhynchus mykiss Walbaum) production system and a hydroponic treatment unit, growing 'Ostinata' lettuce (Lactuca sativa L.) and sweet basil (Ocimum basilicum L.), capable of reducing phosphorus concentration levels in the fish farm effluent to less than 0.1 mg/L. The integration of the fish and plant production system (aquaponics) produces economic cost savings over either system alone. Shared cost savings come from spreading out operating costs (e.g., management, water, nutrients, and overhead charges) and capital costs (e.g., backup generator, used truck, and office equipment) over the two systems. The investment analysis demonstrates the profitability of this combined system over its 20-year expected life. Net present values are positive for a wide range of discount'rates. Internal rate of return analysis shows that for a total investment of $244,720 this system can potentially provide a return of 12.5%. The hydroponic system drives the potential profitability of the combined system with 67% of annual returns derived from plant production.

The use of structural dynamic models to explain successes and failures of biomanipulation

A development of a structural dynamic model, i.e. a model with current change of the most important parameters according to a goal function, is presented with the aim to explain the structural changes observed in lakes, when the nutrient concentration is increased or decreased. This type of models may be important in lake management as it may be possible qualitatively to predict the success or failure of biomanipulation. The answer to the crucial question: at which phosphorus level will the success of biomanipulation be most probable?' will probably require the development of model which takes into account site specific processes and properties, i.e., a more complicated model. As goal function is proposed the thermodynamic function, exergy, which is defined as the work content of the system (model) compared with the system at thermodynamic equilibrium. It is shown that the structural dynamic modelling approach has been able to explain the shift from large to small zooplankton species at a certain level of phosphorus concentration, accompanied by a shifts from a dominance of zooplankton, and predatory fish to a system dominated by planktivorous fish and phytoplankton. The shift in zooplankton species cannot be explained by application of catastrophe theoretical models, which have been used to explain the hysteresis reaction. The results show that the shift should be expected at approximately 0.12 mg P l-1 and that a typical hysteresis reaction occurs at this concentration in accordance with the expectations. These results are consistent with many observations but should be interpreted with great caution, as the model is simple and general and don't account for a number of processes which may influence the results significantly in specific lake studies. The structural dynamic approach has previously been used in ten case studies with good agreement with the observations, but more case studies are needed before a general recommendation of the use of this type of models can be given. The results from this study point toward to apply this type of models for lake management where biomanipulation is involved, although it should be recommended to improve the presented general model with introduction of site specific properties for a considered lake study.

Automated colorimetric determination of phosphorus in silicate rocks in the presence of silicon

An automated colorimetric method has been developed for the determination of phosphorus as the blue molybdophosphate complex in the presence of silicon following fusion of rock samples with sodium hydroxide or a lithium tetraborate-lithium carbonate mixture. Operating conditions and reagent concentrations have been chosen so as to achieve complete reaction and high sensitivity. Values obtained for some French reference standards, using the proposed method, compare well with recommended values and those using the molybdovanadate method. Coefficients of variation of less than 2.5% and 1% were obtained for phosphorus concentration levels [expressed as phosphorus(V) oxide] of 0.15 and 0.5%, respectively.