Definitions and typical values assumed for the generator’s parameters.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). From 2015 he has been appointed chairman of World Conference of Innovative Animal Nutrition and Feeding (WIANF).\r\nHis main research areas are related to pig and poultry nutrition: elimination of harmful effects of heat stress by nutrition tools, energy- amino acid metabolism in livestock, relationship between animal nutrition and quality of animal food products (meat).",institutionString:"University of Debrecen",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Debrecen",institutionURL:null,country:{name:"Hungary"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"25",title:"Veterinary Medicine and Science",slug:"veterinary-medicine-and-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7144",title:"Veterinary Anatomy and Physiology",subtitle:null,isOpenForSubmission:!1,hash:"75cdacb570e0e6d15a5f6e69640d87c9",slug:"veterinary-anatomy-and-physiology",bookSignature:"Catrin Sian Rutland and Valentina Kubale",coverURL:"https://cdn.intechopen.com/books/images_new/7144.jpg",editedByType:"Edited by",editors:[{id:"202192",title:"Dr.",name:"Catrin",surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50451",title:"Control and Estimation of a Variable Pitch Wind Turbine for Maximum Power Point Tracking",doi:"10.5772/62723",slug:"control-and-estimation-of-a-variable-pitch-wind-turbine-for-maximum-power-point-tracking",body:'\nThe extraction and regulation of the power from the wind by a wind turbine followed by the capture of this power by a generator has been the subject of several recent research investigations. The use of a doubly fed induction generator (DFIG) is one of most popular options for large-scale electromechanical conversion of wind power to electrical power. The DFIG employs a two-sided controller, a rotor-side controller (RSC) to control the speed of operation and the reactive power, and a grid-side controller (GSC) using a grid-side voltage source converter which is responsible for regulating the DC link voltage as well as the stator terminal voltage. The rotor-side controller is expected to (i) minimize or regulate the reactive power and hold the stator output voltage frequency constant by a form of current control, (ii) regulate the rotor speed to maintain stable operation, and (iii) alter the speed set point to ensure maximum wind power capture. The role of the grid-side controller is to ensure regulation of the DC voltage bus, and thereby indirectly control the stator terminal voltage. In the case when the generator is feeding an AC-grid, it can be designed to control the power factor. In a typical system, the stator phase voltages and the stator, rotor, and grid phase voltages are assumed to be measured. It is usual to connect the grid-side converter to the grid via chokes to filter the current harmonics. An AC-crowbar is generally included to avoid DC-link over-voltages during grid faults.
\nIt is well known [1] that only a fraction of the power available in the wind is captured by a wind turbine. There is further reduction in the actual power converted to useful power by the generator. The fraction of the power captured by the wind turbine, which is theoretically limited by the so called Betz limit (about 58%), known as the power coefficient is primarily a function of the tip speed ratio, and is usually less than a certain peak value which is about 45% [1]. Maximum energy conversion is possible when the turbine operates at an optimum tip speed ratio which depends on the variation of the power coefficient with respect to the tip speed ratio. The relationship between the power coefficient and the tip speed ratio can be best determined experimentally. In the case of most of the current horizontal axis wind turbines operating at optimum speed, this can be accomplished by indirect control of the rotational speed. The indirect control of the speed is realized by directly controlling the reaction torque of the electric generator [2]. When the principal variables can all be measured, then one could employ one of a large number of maximum power point tracking (MPPT), algorithms have developed. The concept of maximum power point tracking was first introduced in the design of solar panels for spacecraft in the 1970s with the objective of maximizing the power transfer from the photo-voltaic power sources.
\nIn a recent paper [3], the design of a nonlinear rotor-side controller for a wind turbine generator was developed based on nonlinear, H2 optimal control theory. The objective was to demonstrate the synthesis of a maximum power point tracking (MPPT) algorithm for transferring the maximum power from the turbine to the generator. In the case of a doubly fed induction generator, it was essential that the rotor-side controller and the MPPT algorithm are synthesized concurrently as the nonlinear perturbation dynamics about an operating point is either only just stable or unstable in most real generators. The algorithm uses a non-linear estimation technique and maximizes an estimate of the actual power transferred from the turbine to the generator. It is validated by simulating the wind-turbine’s dynamics. In ref. [3], the estimation method was based on the unscented Kalman filter (UKF) and compared with the traditional extended Kalman filter (EKF). The implementation of the algorithm required modelling the real wind velocity profiles from a broadband white noise generator, and by using low order spectrum shaping filters that are derived from approximations of the Kaimal wind velocity spectrum. The simulation was completely executed in the MATLAB environment. The simulation was based on executing the UKF estimator as well as numerically integrating step by step, in parallel, the process model with the feedback controller included in the dynamic model, about the steady equilibrium solution without linearizing the dynamics. The MPPT algorithm was successfully demonstrated in cases when significant levels of wind disturbances are present. In particular the actual power transferred is compared with maximum available power in the wind, and it is shown in ref. [3] that the maximum power is transferred from the wind to the generator by the turbine.
\nWhen applying this algorithm to a real wind turbine, it was found that for purposes of ensuring that the turbine was not overloaded, the collective pitch angle of the turbine’s blades could be controlled so as to be able to limit the maximum power captured by the wind turbine. When the pitch of the blades is controllable there are two control inputs to consider. While the blade pitch angle can be used to regulate the capture of the power from the wind by the wind turbine rotor, controlling the generators reaction torque allows for the power to be smoothly converted into electrical energy. For such variable pitch wind turbine, it was found that in order to implement the algorithm developed in ref. [3], it was essential to either measure the blade pitch angle or the torque on the turbine shaft, which is then used to estimate the true wind turbine aerodynamic torque and the blade pitch angle. In the latter case only a model of the closed loop collective pitch angle dynamics is essential. As there were no other benefits of measuring the blade pitch angle, the second option was preferred. The blade pitch angle was then considered as an unknown input to the torque, and it was estimated from the measurements.
\nIn this chapter the modified MPPT algorithm, in the presence of unknown inputs to the aerodynamic torque, is successfully demonstrated both in the case when no disturbances were present, as it is a prerequisite for successful implementation, and in cases when significant levels of wind disturbances are present.
\nThere have been a number of papers on the subject of modelling of a wind turbine driving an induction generator under turbulent or stochastic wind conditions [4–7]. In this section the electro-mechanical model used in this study which is identical to the model used in ref. [3], is briefly summarized.
\nThe mechanical model of the wind turbine is described by,
where, as defined in ref. [3], ωm is the mechanical speed of the generator shaft, Twt is the torque extracted by the turbine from the wind, Tmel is the mechanical equivalent of the electromagnetic load torque, Jeq is the equivalent total inertia of the generator shaft and Beq is the equivalent total friction coefficient. The electromagnetic load torque Tmel = (P/2)Tel is a linear function of the number of poles P and may be estimated from the electromagnetic reaction torque of the electric generator per pole pair Tel. The torque extracted by the wind turbine Twt is related to the total power absorbed by the turbine from the wind which may respectively be expressed as,
In the above expression ρ is the density of the air at the hub of the turbine, R is the rotor radius, C\n\np\n(λ) is a power coefficient which is a function ofλ = Rω\n\nm\n/U, the tip speed ratio and U is the wind velocity. Thus, the mean torque may be expressed as,
\nThere are several approximations [8, 9] of Cp(λ) in use and a typical approximation in terms of the blade collective pitch angle θ is given by,
\nTypically depending on the approximation used the maximum power coefficient varies over the range, 0.44 ≤ C\n\np\n(λ)max ≤ 0.492 and the corresponding tip speed ratio varies over the range, 6.9 ≤ λmax ≤ 8.8.
\nThe dynamics of variable pitch wind turbine blades plays a key role in the capture and regulation of the power from the wind by the wind turbine rotor. In the case of a horizontal axis wind turbine, there are up to five blades which are assumed to be equi-spaced and to lie with the plane of the rotor disc. The most popular choice for most variable pitch wind turbines is a three bladed rotor. The dynamics of a variable pitch wind turbine blade can be expressed either in a frame that is fixed in the blade or in a frame that is fixed to the rotor disc. It is convenient to represent the aerodynamic forces in a frame fixed to the blade, while the wind inputs and gusts are more easily represented in a frame fixed to the rotor disc plane. In most practical horizontal wind turbine designs, the rotor disc plane usually aligns itself normal to the wind direction. Thus both frames of reference are used in the dynamical analysis of wind turbines and are transformed from one to the other as and when this is required.
\nThe rotor dynamic model is typically described in terms of non-dimensional quantities so that the general rotor configurations can be analyzed without the need to specify the size. Because of the similarity between the mechanical designs of the rotor for a helicopter, the development of the model closely follows the methodology outlined by Padfield [10] and Fox [11]. The important rotor blade properties of interest are the aerodynamic forces and moments acting on the individual blades, as well as the rotor thrust and torque which are related to the blade forces. Each blade is assumed to be fully controllable in pitch with the root of the blade offset from the rotor axis. The Lock number is an important non-dimensional aerodynamic parameter, and is used to characterize the rotor dynamics parameters. The aerodynamically coupled flap-pitch equations of motion of a single blade are derived in a rotating frame as function of the azimuth angle. To derive the equations of motion of all the blades as a single unit, the coefficients in the equations may be expressed in terms of the so called multi-blade coordinates. This is done by expanding all trigonometric functions such as products of sine and cosine functions as the sums of relevant sine and cosine terms. Thus the fixed frame equations of motion obtained by applying multi-blade coordinate transformations will represent the dynamics of the rotor disc containing N blades. The model of the inflow dynamics is based on the finite state approximation developed by Pitts and Peters [12]. The wind is responsible in generating the primary moment acting on the rotor resulting in a rotor torque, which produces the dominant component of the moment about the rotor shaft axis, which is converted into electrical energy by the generator. This torque can be obtained by integrating the moments of the in-plane aerodynamic pressure distribution acting on the blades about the shaft axis.
\nBroadly, the approaches to pitch control may be classified into two groups. In a direct pitch controlled system, the controller monitors the wind-turbine’s power output at every sampling instant. When the power output exceeds an upper bound, the blade pitch is altered to lower the power generated by the turbine. Increasing the pitch attitude generally reduces the power output. When the maximum power output of the turbine is within the safe operating limits, the pitch angle is reduced to zero.
\nThe second approach to pitch control involves operating the wind turbine with the blades pitched at angle just below the stall angle. The geometry of the blade profile and twist, however are aerodynamically tailored to ensure that when the induced wind speed is high, the angle of attack also increases and the blade begins to stall. The stalling of the lift generated restricts the magnitude of the lift generated and consequently the power generated is also limited. In an actively stall controlled turbine, the pitch of the blade is maintained just below the critical stall angle as long as the power generated by the wind turbine is within the safe operating limits, and increased beyond the critical value when it is desired to stall the generation of lift on the blade. Thus, when the generator is overloaded, the controller will pitch the blades in the opposite direction from what a pitch controlled machine does, in order to make the blades go into an increased state of stall.
\nThe approximation to C\n\np\n(λ) given by equations (4a) and (4b) are only valid or small increments in the blade pitch angle, and do not include the influence of blade stall. For this reason, in this work, we consider only a pitch controlled wind-turbine. To model the blade dynamics, it is first required to model the open loop dynamics. Once the open loop pitch dynamics is obtained, it is assumed that an appropriate feedback law is designed. Thus, what is important is the closed loop dynamics of the pitch angle which is represented by a reduced, first order model of the form,
Thus the discrete dynamics of the pitch angle may be expressed as,
The model may be used to design control laws for both active pitch controlled and active stall controlled wind-turbines. The demanded blade pitch angle θdemand, is set by the unconstrained minimization of the square of the error between the desired output power and the actual power generated by the wind turbine rotor.
\nTo design an active stall controller, the first step is to model the section lift and drag coefficients of the blade when the section angle of attack exceeds the stall angle. Modes of both the section lift and drag coefficients of the blade when the section angle of attack exceeds the stall angle have been presented by Tangler and Kocurek [13] and by Tangler and Ostowari [14] based on a model developed by Viterna and Corrigan [15]. These are then substituted into the expression for the power coefficient developed on the basis of the blade element momentum theory (see for example Vepa [16], Section 4.4.1). Once the expression for the power coefficient is found, the commanded blade angle is found by requiring the error between the actual power generated, estimated from the power coefficient, and the maximum power is a minimum.
\nThe basic equations of the dynamics of the doubly fed induction machine can be established as done in ref. [3], by considering the equivalent circuit of a single stator phase and a single rotor phase and the mutual coupling between the stator and rotor phases. The voltage vector consisting of the voltages applied to each stator and rotor phases is related to the voltage drops across the resistances of these phases and the rate of change of the fluxes linking the stator and rotor phases. The fluxes in turn are related to the current vector via a matrix of inductances which are not constant but period functions of time with the period equal to the rotor’s electrical speed, ω\n\ne\n = Pω\n\nm\n, which is the product of the number of pole pairs, P and the rotor’s mechanical speed, ω\n\nm\n. When all of the stator and rotor quantities are transformed to a stationary frame (the d–q frame) using the standard Park-Blondel transformation, in terms of the stator’s and rotor’s voltage oscillation frequencies ω\n\ns\nand ω\n\nr\n respectively, the dynamic equations reduce to a set of four with constant coefficients as derived in ref. [3]. Moreover ω\n\nr\n = ω\n\ns\n − ω\n\ne\n can be found by measuring ω\n\ns\n and ω\n\ne\n, and is also the slip frequency. (The ratio s = ω\n\nr\n/ω\n\ns\n is the slip.)
\nRelationships between the d–q frame and the stator and rotor phases.
The phase angles relating the directions of the d–q frame and the phase angles of the first of the three stator phases, A, B, and C, θs and the first of the three rotor phases, a, b and c, θr satisfy the relation θr = θs − θe where θe is the rotor’s electrical angle as illustrated in Figure 1. In modeling the stator of DFIG, the generator convention that positive direction of electromagnetic torque is in the direction opposing to the direction of rotation is used, while in modeling the rotor of DFIG the motor convention is used. The dynamic equivalent circuit of generator in synchronous rotating reference frame, the d–q frame, is used to set up the model equations.
\nThe dynamical equations of the DFIG relating the voltages in the stator and rotor and in the d–q frame to the currents, fluxes and the flux rates are,
The stator fluxes are related to the stator and rotor currents in the d–q frame as,
The rotor fluxes are related to the stator and rotor currents in the d–q frame as,
In the above equations, as defined in ref. [3], Ls,Lr,Rs, and Rr, are respectively the self-inductances and resistances of the stator and rotor windings. The quantity Lm is the mutual inductance between a stator and a rotor phase when they are fully aligned with each other.
\nAt the stator terminals, the active and reactive components of the power are given by,
At the rotor terminals, the active and reactive components of the power are given by,
The active and reactive powers exchanged by the generator and the grid are respectively the sum of the active and reactive components of the power at the stator and rotor. The electromagnetic reaction torque may be expressed as,
Assuming that the stator flux is stationary in the d–q frame and neglecting the stator’s resistive voltage drop vds = 0. Hence the q – component of the stator voltage may be expressed as, vs = vqs. Assuming further that a grid-side controller is in place and choosing a stator-flux oriented reference frame, the d-axis is aligned with the stator flux linkage vector, φs; thus φds = φs and φqs = 0. From equations (8a) and (7a), ims is defined by,
Thus, from equations (8a),
Eliminating ids and iqs from equations (7b) and (8b) it can be shown that, the rotor flux components are,
The electromagnetic reaction torque given by equation (10) and the reactive power at the stator terminal given by the second of equations (9a) may also be expressed in terms of i\n\nms\n as,
Defining the mutual inductance coupling coefficient σ as,
and using equations (11c) with vds = 0 and vs = vqs the rotor voltage equations given by the equations (7b) are expressed as,
The total reactive power is Q = Q\n\nr\n + Q\n\ns\n where Q\n\ns\n is given by equations (11a) and (12b), with v\n\nds\n = 0 and v\n\ns\n = v\n\nqs\n. Thus the total reactive power is,
The definitions of the resistances and inductances and their typical values assumed in this chapter are listed in Table 1.
\nP | \nNumber of poles | \n6 | \n
---|---|---|
\nR\n\ns\n\n | \nStator resistance | \n0.95 Ω\n | \n
\nL\n\ns\n\n | \nStator inductance | \n94 mH\n | \n
\nL\n\nm\n\n | \nMagnetizing inductance | \n82 mH\n | \n
\nR\n\nr\n\n | \nRotor resistance | \n1.8 Ω\n | \n
\nL\n\nr\n\n | \nRotor inductance | \n88 mH\n | \n
\nV\n\na\n\n | \nStator phase voltage | \n380 V | \n
\n | Grid frequency | \n50 Hz | \n
\n | Nominal mechanical rotor speed | \n100 rads/sec | \n
\n | Rated maximum power | \n100 kW | \n
Definitions and typical values assumed for the generator’s parameters.
In steady state, assuming that,
\nwhere the superscript ‘0’ refers to the steady-state condition, and subtracting the steady-state components from (14a) and (14b), the electro-mechanical perturbation equations are obtained. The perturbation states, inputs and variables are defined as:
\nGiven that \n
Using equation (11a) and introducing the steady state and perturbation variables, the expression for the electromagnetic torque is,
The wind turbine perturbation torque \n
where the first component is evaluated at the current rotor speed, ω\n\ne\n and the second at the mean wind speed, U\n0. Hence, without making any assumptions that the perturbations are small, the equation for mechanical motion, equation (18), may be expressed as,
while the electrical machine perturbation equations are,
Assuming that
equations (21) and (22) may be partially decoupled, and (22a) may be treated independently. Thus the complete non-linear equations for the perturbation states used for the design of the nonlinear rotor-side controller may be expressed in state space form as,
where
is the disturbing angular acceleration on the rotor due to wind speed fluctuating component and A is a matrix of functional coefficients.
\nR | \nWind turbine blade disc radius | \n6 m | \n
---|---|---|
\n | Number of blades | \n3 | \n
\nU\n0\n | \nNominal wind speed | \n10 m/s | \n
\nP\n\nn\n\n | \nWind power at nominal wind speed | \n~10 kW | \n
\n | Gearbox ratio | \n10 | \n
\nJ\n\neq\n\n | \nRotor inertia | \n40 kgm2\n | \n
\nB\n\neq\n\n | \nViscous friction coefficient | \n0.07 Nms/rad | \n
\n | Cut-in wind speed (m/s) | \n3.5 m/s | \n
Definitions and typical values of the wind turbine parameters.
The modeling of the turbulent wind component and the control of wind turbine is based entirely on [3] and will not be repeated here. The complete characteristics of the wind turbine are summarized in Table 2.
\nThe dynamic model of the wind turbine that must be employed for purposes of state estimation is not only not linear but also involves the estimation of large dynamic signals. The Kalman filter which was formulated in the 1960s is primarily applicable to linear systems. To overcome the limitations imposed by the requirement of linearity, it was subsequently, empirically, extended and applied to nonlinear systems. A number of approaches such as the extended Kalman filter (EKF) have been proposed in the literature to extend the application of the traditional Kalman filter for nonlinear state estimation. However, the stability of these extended formulations is not guaranteed unlike the linear Kalman filter. Thus the EKF may diverge if the consecutive linearizations are not a good approximation of the linear model over the entire uncertainty domain. Nonetheless the EKF provides a simple and practical approach to dealing with essential non-linear dynamics.
\nThe UKF has been proposed by Julier, Uhlmann, and Durrant-Whyte [17], and used in ref. [3]. It can overcome the limitations of applying the Kalman filter to nonlinear systems. The UKF based on the unscented transformation of the statistics of a random variable. It provides a method of calculating the mean and covariance of a random variable undergoing a non-linear transformation y = f (w). In the main, the method is used to construct a set of sigma vectors and propagate them through the non-linear transformation. The mean and covariance of the transformed vector are approximated as a weighted sum of the transformed sigma vectors and their covariance matrices. The details may be found in the paper by Julier and Uhlmann [18].
\nAs in ref. [3], given a general discrete nonlinear dynamic system in the form,
where x\n\nk\n ∈ R\n\nn\n is the state vector, \n
It is possible to estimate the unknown inputs only when the matrix product \n
Several algorithms for achieving maximum power tracking and control have been proposed for a number of power systems [21, 22]. There have been a number of MPPT controllers proposed recently for wind turbines based on maximizing the net power captured by the generator [23–26]. A recent book on the topic has covered the optimal control based strategies quite extensively [27]. There have also been a few methods based on some form of optimal estimation of the wind speed [28]. A nonlinear controller based MPPT method has also been applied to wind turbines [29]. Several of the optimal control strategies may be efficiently implemented for a wind turbine provided that highly reliable non-linear estimation algorithms are used to estimate the states of the wind turbine in operation. In this section, one such approach is briefly outlined and implemented as in ref. [3]. The system now includes the independently controlled variable pitch blades while in ref. [3], only a turbine with fixed blades was considered.
\nIt is assumed that the induction machine is controlled in a manner so as to ensure variable-speed operation over a wide range input conditions, so it is possible to exercise direct control of the system’s tip speed ratio. The wind power captured by the wind-turbine is estimated from the state estimates by the equation, \n
The wind turbine torque T\n\nwt\n is a weighted linear combination of T\n\nel\n, dω\n\ne\n/dt, and ω\n\ne\n. When \n
The condition for maximum power capture is,
Thus the instantaneous torque speed ratio or the instantaneous impedance is equal to the negative of the incremental torque to incremental speed ratio or the incremental impedance. This is frequency dependent and is determined when the wind power input is a maximum. It follows that in the nonlinear perturbation equations,
In evaluating \n
To determine the rotor frequency at which maximum power is extracted from the wind by the turbine, the rotor frequency is assigned an initial value \n
The frequency \n
where \n
To make the comparisons easy and to draw meaningful conclusions, the same example as the one considered in ref. [3] is also considered here with the exception that, in the case considered here, the blade pitch angle was assumed to be independently controlled. The initial equilibrium conditions were deliberately chosen so the nonlinear perturbation dynamics of the turbo-generator about the initial operating point were not stable. So the initial feedback controller was obtained by adopting the LQR-based methodology of Vepa [3] and using a model evaluated at the initial perturbation. Measurements of the rotor speed and the rotor d–q currents were generated by adding a random error with zero mean and a specified variance to the simulated outputs. All the perturbation states were estimated using both the UKF and the traditional EKF methodologies. Figure 2 illustrates the simulated electrical speed of the generator, which is thrice the mechanical speed, for a time step, dt = 0.001 s and compared with the UKF and EKF estimates over a time frame of 20,000 time steps or 20 seconds in real time. The maximum error between the curves is less than 0.2%.
\n\nFigure 3 compares the electrical speed error in the measurement, with estimates of it obtained by using the UKF and the EKF. To make the comparison we have zoomed-in over a time frame of the first 50 time steps. Quite clearly the UKF estimate converges rapidly to the measurement while the EKF estimate fluctuates in the vicinity of the measurement. From the comparisons shown in Figure 3, the superiority of the UKF over the EKF can be deduced. For purposes of maintaining clarity, all the other results corresponding to the EKF estimates are not shown in the figures.
\nSimulated electrical speed (in rads/s) response of the DFIG generator compared with the measurement, the UKF and the EKF estimates.
Using this algorithm repeatedly has accentuated the need for making accurate electrical speed rate measurements and estimates. The electrical speed rate estimation was done using measurements of the electrical speed rate. From the previously estimated electrical speed and independently processing the measured electrical speed rate in another first order mixing filter, the estimates of the electrical speed are continuously updated. This approach provides precise estimates of the speed rate and facilitates the accurate estimation of the torque absorbed by the turbine from the wind. Figure 4 shows the corresponding power transferred from the wind to the generator over the first 20,000 time steps and compared with maximum available wind power at that particular maximum magnitude of the wind speed and zero blade pitch angle.
\nComparison of the measured and UKF and EKF estimated electrical speed (in rads/s) errors corresponding to Figure 2.
Wind power transferred to the generator over the first 20,000 time steps and compared with maximum available wind power.
Turbine power characteristic over the operating speeds in rad/s.
\nFigure 5 illustrates the power speed characteristic corresponding to Figure 4. From Figures 4 and 5, it may be observed that the power transferred by the turbine from the wind to the generator tracks the maximum available wind power. Figures 6 and 7 illustrate the corresponding torque on the generator and the torque-electrical speed characteristic. Also shown on these figures is the torque corresponding to the maximum available power.
\nTurbine torque over the first 20,000 time steps and compared with the torque at the maximum available wind power.
Turbine torque characteristic over the operating speeds in rad/s.
\nFigure 8 shows the growth of the estimated blade pitch angle and compared with the simulated blade pitch angle.
\nEstimated and simulated blade pitch angles compared.
In this chapter, a nonlinear UKF is used to provide the rotor-side control inputs and also in a tracking controller that ensures that the desired maximum power operating point is exactly tracked. As in ref. [3], the uncontrolled DFIG is unstable. Thus this necessitates the use of a stabilizing controller prior to implementing a MPPT filter. The MPPT filter tracks the maximum power point as the power is transferred from the wind to the turbine. The rotor-side control laws are synthesized by employing a H\n2 optimal control law as described in ref. [3]. The MPPT filter is proposed and validated using non-linear UKF and EKF based estimation techniques. Thus an estimate of the actual power transferred from the turbine to the generator is maximized. It is shown that the MPPT filter can operate alongside the controller for regulating the blade pitch angle. The state estimation method is based on the UKF which is compared with the traditional EKF and is shown to be superior. The validation of the algorithm is carried out by simulating real wind velocity profiles from a white noise generator and using low order spectrum shaping filters that are derived from approximations of the Kaimal wind velocity spectrum. The MPPT algorithm is successfully demonstrated in the case significant levels of wind disturbances present and with the independently controlled variable pitch blades.
\nThe advantages of using stochastic optimal control theory and nonlinear optimal control are discussed in ref. [3]. In this chapter it has been demonstrated that the MPPT control filter which acts as an outer-loop controller, continuously seeks to maximize the power absorbed by the wind turbine while the inner loop estimator continuously estimates and updates the states including the blade pitch angle (which was held fixed in ref. [3]). The MPPT control filter included a feedback signal estimated using the Newton-Raphson formula at each time step, just as in ref. [3]. One can estimate the wind power captured by the turbine and it indicates that the filter is seeking to operate within 5–9% of the simulated operating maximum power point by controlling the speed of the rotor after a 0.5 s delay, which allows the UKF to estimate the states and the unknown blade pitch angle without a significant error and to eliminate the influence of power transients. The maximum available power in the wind is computed, as in ref. [3], by assuming that the wind is frozen at its maximum magnitude before it encounters the blades and the deterministic formula for the maximum power coefficient C\n\np\n(λ)max, is used to calculate it. In steady state the estimated maximum turbine power was generally uniformly less than the maximum available power, the difference being accounted for by the losses due to the finite and variable blade pitch angle, and the losses due to viscous frictional torques.
\nFinally the MPPT algorithm, based on the nonlinear state estimation using the UKF, is shown to perform, even when the blade pitch angles are dynamically varied and the introduction of the blade dynamics does not cause any additional instabilities when compared with the case of fixed blades considered in ref. [3]. The maximum power transfer achieved is less than in ref. [3], unless the blade is assumed to be fixed with the pitch angle set at zero.
\nSulfide ore minerals are generally concentrated by milling and flotation, which produces tailings containing gangue minerals and residual sulfides. Milling involves crushing and grinding to reduce particle size and liberate ore minerals from the rock matrix. After ore minerals have been extracted and concentrated, the resulting tailings are commonly dewatered and deposited in sub-aerial tailings, impoundments, or stockpiles [1]. They are piled up as less than 5 cm thick layers and are slightly differentiated by compositional and/or granulometric features. Although the metal content is removed in the metallurgic process, some ore sulfides (e.g. pyrite, galena, sphalerite, chalcopyrite, arsenopyrite, etc.) can be deposited, either because they were not sufficiently high-grade for use, or due to a deficient extraction technology. They entail both an accumulation and a potential emission source of trace elements (e.g., Cu, Fe, Pb, and Zn). Oxidation of the sulfide minerals accumulated in the abandoned mine tailings may cause: (a) highly contaminating acid mine drainage (AMD) from leakages and (b) mobilization of significant quantities of trace elements such as As, Cd, Cu, Hg, and Pb. It becomes necessary to identify and characterize these hazardous areas where large quantities of potentially toxic elements can be released into the environment. Mine ponds are, therefore, an important environmental problem, especially if they are abandoned.
\nSpain has a long mining tradition dating from pre-historic times up to the present day. A large amount of mine installations, galleries, and waste deposits were abandoned until the 1980s by the cessation of mining activity. Pollution from these sources can originate via mining spills, leakages, or wind-blown dust, and toxic elements with a high mobility can cause huge environmental problems: accumulation in flora and fauna, reducing the quality of streams and groundwater.
\nAn inventory of the abandoned mine waste deposits has been prepared by the Spanish Ministry of the Economy, through the Directorate General of Energy Policy and Mining [2]. The most relevant contribution of the inventory consists in the classification of the existing mine waste deposits based on their hazard potential for infrastructure and the human population. Another significant aim is focused on providing a qualitative geotechnical and environmental assessment of the elements at risk and an associated description. Further knowledge of the current status of the highest potential risk deposits is required because of the preliminary nature of the inventory, carried out by means of visual surveys and without sampling or testing.
\nIn summary, a complete geoenvironmental characterization of the affected areas is crucial for any proposal of effective measures that could help to minimize environmental impact and concern.
\nSeven mine areas from the most important metallic mining district in Spain have been selected (Figure 1): La Naya, Monte Romero and Mina Concepción from Iberian Pyrite Belt [3, 4], Brunita from Cartagena-La Unión [5], San Quintín from Alcudia Valley [6], and San Cristóbal and Las Moreras from Mazarrón [7].
\nLocation and field photographs of the different studied mine areas: (a) location of the sulfide mine ponds in Iberian Pyrite Belt (LN—La Naya, MC—Mina Concepción, and MR—Monte Romero), Cartagena-La Unión (BR—Brunita), Alcudia Valley (SQ—San Quintín), and Mazarrón (SC—San Cristóbal and LM—Las Moreras); (b) Monte Romero; (c) acid leakage from Mina Concepción tailings; (d) detail of tailings in La Naya; (e) La Naya; (f) tailing dune in San Quintín; (g) Las Moreras; (h) borehole core samples from Brunita tailings; (i) San Cristóbal; and (j) Brunita.
One of the largest concentrations of massive sulfide mineralizations in the world is hosting at the volcano-sedimentary rocks of the Iberian Pyrite Belt, in the southwest (SW) of the Iberian Peninsula. The main ore mineral is pyrite, although lower quantities of sphalerite, galena, chalcopyrite, and arsenopyrite are also found. An intense mining activity in this province is related to the exploitation of S, Cu, Pb, Zn, Ag, and Au from the sulfide ore minerals. Different studies have pointed out the significant concentrations of certain trace elements in sediments and soils surrounding mining or waste sites in the Iberian Pyrite Belt district [8]. The waters of the Tinto and Odiel fluvial systems are also affected [9]. Remediation has only been conducted in a few of the mine areas, although they still display significant environmental issues [4]. In 1998, an environmental disaster occurred in the SW of Spain, when the tailing dam of one of the bigger mines from the district was ruptured. Around 2 × 106 m3 of heavy metal-bearing sludge and ~4 × 106 m3 of acidic waters were released [10].
\nMonte Romero (Figure 1) comprises two mine ponds located at the Cueva de la Mora mine site, where Pb- and Zn-bearing minerals were benefited [3]. La Naya (Figure 1) is a mine pond located to the southeast of Minas de Riotinto town, and is one of the largest deposits mined during the extensive works in this mining group. The main ores extracted were Cu-bearing minerals and pyrite. Another mine site studied was Mina Concepción, a restored mine pond located on the SE of the Almonaster la Real village (Figure 1). Tailings from the metallurgical treatment and benefit of pyrite were piled up over metavolcanic lithologies [4]. The contention dyke retaining them is 8 m high, and three collector pipes going through the dyke controlled the drainage. The physical restoration and the landscape integration of the mine pond were achieved by sealing and reforestation with pine trees, substituting the original vegetation.
\nOne of the most significant places of geochemical pollution and geotechnical instability in Spain’s abandoned mining heritage is the Cartagena-La Unión district, southeast of Spain [2]. The Brunita mine pond is one of the numerous tailing deposits in the district, affecting the surrounding watercourses that reach La Manga coastline, a major tourism location in SE Spain. Human beings, fauna, flora, groundwater, and agricultural soils are negatively stilted [5].
\nThe mine tailings were produced from grinding and metallurgical treatment of mineral from Eloy and Brunite mines between 1952 and 1981 [2]. The main ore minerals are pyrite, sphalerite, galena, marcasite, and pyrrhotite. Other minor sulfides include arsenopyrite, minerals of the tetrahedrite-tennantite group, chalcopyrite, and stannite [11]. In October 1972, an extreme rainfall event caused damage in the Brunita mine pond (Figure 1). A tailing flash flood killed one person and caused serious material damage. As a result, a retaining wall of the coarse-grained tailings was built. Since 1981, when the mine was closed, no further works on restoration or reclamation have been carried out.
\nThe San Quintín abandoned mine area, located at the Alcudia Valley (Ciudad Real province, Spain), is crossed by the Don Quixote Route [12], a tourist set of itineraries created in 1995 to celebrate the IV Centenary of the publishing of “El ingenioso hidalgo Don Quijote de La Mancha”. This route, the longest ecotourist route in Europe, was recently declared as Cultural Itinerary by the Council of Europe. It could soon reach the rank of Humanity Heritage because of its high cultural and environmental quality. These features make the San Quintín area a busy tourist route, and the environmental characterization of potential hazards is so necessary.
\nThe ore mainly comprised Ag-bearing galena and sphalerite as major phases of a hydrothermal mineralization also including pyrite, chalcopyrite, marcasite, pyrrotine, bournonite, siderite, boulangerite, and ankerite [13]. The exploitation was performed from 1887 to 1934, date of the mining closure. In 1973, a new treatment plant was installed for re-working of approximately three million tons of tailings. Several tons of cinnabar from the Almadén mine (Ciudad Real, Spain) were experimentally treated in this new plant with successful results. At present, several mine tailings resulting from re-working together with the ruins of the mine structures are clearly visible. AMD from the tailings is recognized. Furthermore, a tailing dune, formed over one of the ponds, is migrating and toward the agricultural soils surrounding the mine area (Figure 1). The course of the Arroyo de la Mina stream, crossing the mining area, was altered and presently runs along the limits of the mine ponds.
\nMazarrón is located 4 km from the Mediterranean coast in SE Spain, and was one of the most important mining districts in the area [14]. It was exploited from Roman times to the early 1960s for Pb, Al, Ag, and Zn. Together with mining activities, the Mazarrón area is characterized by intensive farming and tourist pressure. Mining deposits caused significant water and soil pollution, and led to negative effects on both agricultural and tourism land uses. A correct geo-environmental characterization of the affected area is important for any proposal of restoration and remediation focused on minimizing environmental impacts.
\nThe ponds are located on the hill slopes of the San Cristóbal and Los Perules hills, situated near a watercourse that drains to the Las Moreras watercourse, in turn flowing into the Mediterranean Sea (Figure 1). The main ore minerals were sphalerite, pyrite, and Ag-bearing galena. Other minor sulfides were arsenopyrite, chalcopyrite, the tetrahedrite-tennantite group, stibnite, cinnabar, and berthierite. The mine tailing ponds, near the Las Moreras dry watercourse, are situated on Quaternary alluvial and colluvial deposits (Figure 1). Although the total level amount of rainfall is not high, the area is subjected to strong stormy events each year, which can induce flash flooding phenomena.
\nMineralogical and geochemical techniques normally used to determine the composition of mine tailings, soils, waters, and watercourse sediments and the possible occurrence of AMD are described. In the case of high Hg contents, gaseous mercury emissions were analyzed too. Sampling features such as methods, sampling depth, analytical techniques, etc. are summarized in Table 1 and described below.
\nMine district | \nStudy mine | \nOre mineralogy | \nSample type | \nSampling | \nAnalytical techniques | \nComplementary techniques | \n
---|---|---|---|---|---|---|
Iberian Pyrite Belt | \nLa Naya | \nPy, Sp, and Ga | \nTailings | \nManual sampler (2.7 m depth) | \nXRD, ESEM+EDX, INAA, pH | \nERT (1 profile) | \n
Monterromero | \nSp, Ga, and Py | \nTailings | \nManual sampler (2 m depth) | \nXRD, ESEM+EDX, INAA, pH | \nERT (2 profiles) | \n|
Mina Concepción | \nPy, Sp, Ga, Apy, Cpy, and Mg | \nTailings | \nManual sampler (1 m depth) | \nXRD, INAA | \nERT (6 profiles) | \n|
Water | \nLeakage | \nICP-MS, pH, EC | \n||||
Cartagena-La Unión | \nBrunita | \nPy, Sp, Ga, Ma, Pyr, Te, Cpy, Apy, and St | \nTailings | \nBorehole (24 m depth) | \nXRD, ICP-MS, pH, EC | \nERT (6 profiles), aerial photographs | \n
Water | \nWater table | \nICP-MS, pH, EC | \n||||
Alcudia Valley | \nSan Quintín | \n(Ag)-Ga, Sp, Py, Ma, Cpy, and Pyr | \nTailings/colluvial | \nBoreholes (10–12 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \nERT (7 profiles), aerial photographs | \n
Water | \nBorehole (8.5 m depth); watercourse; AMD | \nICP-MS, pH | \n||||
Soil | \nSurround; Blank | \n\n | ||||
Air | \nSummer-winter | \nZAAS-HFM | \n||||
Mazarrón | \nSan Cristóbal | \nPy, Sp, (Ag)-Ga, Cpy, Apy, Te, Ci, and St | \nTailings | \nBorehole (5.5 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \nERT (1 profile) | \n
Watercourse sediments | \n||||||
Water | \nWater table | \nICP-MS, pH, EC | \n||||
Las Moreras | \nTailings | \nBorehole (6 m depth) | \nXRD, ICP-MS, ESEM+EDX, pH | \n|||
Watercourse sediments | \nERT (1 profile) | \n|||||
Water | \nWatercourse | \nICP-MS, pH, EC | \n
Main sampling features of the studied mine district in Spain: Iberian Pyrite Belt, Alcudia Valley, Cartagena-La Unión, and Mazarrón [3, 4, 5, 6, 7].
XRD, X-ray diffraction; ESEM, environmental scanning electron microscopy; EDS, energy dispersive X-ray; INAA, instrumental neutron activation analysis; ICP-MS, inductively coupled plasma-mass spectrometry; EC, electrical conductivity; and ZAAS-HFM, atomic absorption spectrometer with Zeeman effect.
At Brunita, San Quintín, and San Cristóbal-Las Moreras areas, nondisturbed rock drill core tailing samples were collected from boreholes using a rotary drilling machine with a core bit diameter between 86 and 100 mm. Sampling was carried out by digging down below the surface of each pond, eliminating the surficial sealing to prevent falling material inside the borehole during drilling. Sampling depth of the unaltered samples varies between 0.5 and 1 m, depending on the borehole depth. All samples were air-dried for 7 days, passed through a 2-mm sieve, homogenized, and stored in plastic bags at room temperature prior to analyses. Below mine tailings, colluvial sediments (2–4 m) in San Quintín and watercourse sediments in San Cristóbal (2.5 m)—Las Moreras (4.5 m) were drilled, collected, and analyzed to obtain a complete geoenvironmental characterization of the area. Where a rotary drilling machine is not possible to place, samples were collected with an Eijkelkamp soil core manual sampler. Sampling was sequential with a centimeter vertical constant spacing and lower in depth than boreholes. This is the case of tailings studied at the Iberian Pyrite Belt district (Table 1). In many occasions, soils surrounding mine facilities show evident signals of contamination from different sources (tailings, ponds, open shafts, etc.) and pathways (wind erosion, water flows, etc.) affecting different receptors (agricultural soils, colluvial sediments, humans, etc.). In these scenarios, it is necessary to collect representative sample soils from the studied zone, and from a natural soil far enough of the mining area as a background sample (blank), in the case of San Quintín mine area. This is necessary to compare the potentially toxic element contents with the natural amount in the surrounding soils.
\nWater sampling is also necessary to check the metal amount in watercourse affected by the mining operations, as well as the possible AMD generation from the tailings. Several water samples have been collected depending on the features of the studied mine site: water sample collected at 8.5 m depth from a borehole (San Quintín), water samples from the tailing pond (Brunita and San Cristóbal), water sample from the watercourse crossing the mining area (San Quintín and Las Moreras), or water sample from the leakage of an abandoned (San Quintín) and a restored mine pond (Mina Concepción). All water samples were collected in 250 ml plastic bottles, were kept in a refrigerator at 4°C and, prior to analysis, and were filtered with 45 μm pore spacing. In San Quintín mine site (Alcudia Valley district), total gaseous mercury (TGM) was also measured using a 200 m sample spacing grid during both summer and winter by means of an atomic absorption spectrometer with Zeeman effect (ZAAS-HFM). The geostatistical treatment of data was performed applying block kriging to obtain interpolation maps of the study area.
\nMineralogical characterization of borehole and soil samples was performed by X-ray diffraction (XRD) using a Philips X’Pert powder device with a Cu anticathode and standard conditions: speed 2° 2θ/min between 2° and 70° at 40 mA and 45 kV. The whole sample was examined by crystalline nonoriented powder diffraction on a side-loading sample holder. Semi-quantitative results were obtained by the normalized reference intensity ratio (RIR) method. The mineralogy of the samples was also studied by environmental scanning electron microscopy (ESEM), coupled with energy dispersive X-ray analysis (EDX), using a Philips XL30 microscope. The ESEM was operated at a low-vacuum mode, at a pressure between 0.5 and 0.6 Torr under a water vapor atmosphere and an operating voltage of 20 kV. The XRD and ESEM-EDX analyses were performed at the Centro de Apoyo Tecnológico (CAT Universidad Rey Juan Carlos, Móstoles, Spain). From the total list of major, minor, and trace elements analyzed, Ag, As, Cd, Co, Cu, Fe, Hg, Ni, Pb, S, Sb, Sn, and Zn were specially chosen because of their abundance in these types of sludges and because most of them are included in the priority contaminant list of environmental protection agencies. They were analyzed by total digestion (TD) or lithium metaborate/tetraborate fusion (FUS), inductively coupled plasma-mass spectrometry (ICP-MS), and instrumental neutron activation analysis (INAA) at the Activation Laboratories Ltd. (1428 Sandhill Drive, Ancaster, Ontario, Canada). Quality control at the Actlabs laboratories is performed by analyzing duplicate samples and blanks to check the precision, whereas accuracy is determined using Certified Reference Materials (GXR series; see [15]). Detection limits for the analyzed elements are as follows (data in μg g−1): Ag (0.3), As (5), Cd (0.5), Co (1), Cu (1), Fe (100), Hg (0.005), Ni (1), Pb (5), S (10), Sb (0.5), Sn (1), and Zn (1). Pb content higher than 5000 μg g−1 (above the ICP-MS maximum detection limits) was measured by ICP-OES or atomic absorption.
\nWater samples were analyzed by ICP-MS at Activation Laboratories Ltd. The pH was measured using an electronic pH meter (CRISON) that was calibrated using standard buffer solutions at two points: pH: 7 and pH: 4. This parameter was determined in a slurry system with an air-dried sample (10 g) mixed with distilled water (25 mL). Before reading the pH values, these solutions were vigorously stirred in a mechanical shaker for 10 min and left to stand for 30 min.
\nElectrical resistivity tomography (ERT) imaging is a near surface nondestructive technique designed to be widely used in many different geological applications, including the determination of the materials constituting the bedrock, unraveling the stratigraphical record of the basins and locating hidden faults, among others [16]. A resistivity profile is obtained from many different measurements using different available electrode arrays, with the data acquisition being controlled by means of a computer. These measurements provide data about the variations of apparent resistivity values at different depths, in such a manner that when the spacing between the electrodes increases, the resistivity data correspond to a greater depth of investigation. After data acquisition, the apparent resistivity values are converted to an image of true resistivity variations against depth. The resistivity meter used to obtain the data for this study was a Syscal Junior Switch 48. As mentioned before, different electrode arrays are available, with differences in relation to the depth of investigation and signal-to-noise ratio (e.g. [17]). From the different electrode arrays available, a Wenner-Schlumberger array has been selected because it provides a good penetration depth, the signal to noise ratio is good, and both vertical and horizontal resolutions are also reasonable. Moreover, different authors have previously used this array successfully in several similar studies [3, 4, 5, 6, 7, 18] because it shows a high contrast between the resistivity values of the vase of the mine ponds and the resistivity values of the infilling. From the field data, the information obtained about the resistance measurements between the different electrodes and distances between them is used to calculate the apparent resistivity values. Then, a plot of the apparent resistivity values vs. depth, named pseudosection, is constructed. Previously to be interpreted, the pseudosections need to be converted into profiles where true resistivity values are plotted against depth. The conversion from apparent to true resistivity values is performed by means of the RES2DINV code. As a first step of this inversion procedure, the data are filtered to remove bad data points, and then the topography information along the profile is also included. The code uses the L1 norm for the data misfit and the inversion was performed using the L1 norm (robust) for the model roughness filter [19]. The choice of the robust inversion is justified because this kind of inversion is more accurate when sharp boundaries in the model exist, and this is just the case involved in this study because of the large contrasts expected in the electrical properties of the materials. The method uses a finite element scheme for solving the 2-D forward problem and blocky inversion method for inverting the ERT data. The code RES2DINV finally provides an inverted resistivity image for each profile. The inverted profile is the one used to obtain the final interpretation about the variations of the subsurface lithology.
\nAerial photographs, georeferenced and integrated into a GIS, are used to map morphologic elements and to detect the main changes in the environment through time. The evolution of mine deposits in the Brunita site was carried out to estimate anthropogenic changes in the landscape [5]. We worked with aerial photographs taken in 1929 (photogrammetric flight by Ruiz de Alda), 1946, and 1956 (flights by the Geographic Service of the Spanish Army), and 1973, 1981, 2004, and 2013 (flights by the Spanish National Geographic Institute), and anaglyphs of orthoimages from years 1946, 1956, 1981, and 2004 [20]. On the other hand, the study of a mine pond and a tailing sand dune at the San Quintín mine permitted to evidence the eolian dispersion of contaminants to the surroundings [21]. Several aerial photographs corresponding to different years were also analyzed (1957, Geographic Service of the Spanish army, 1977, 1984, Spanish National Geographic Institute, 2006, digital orthophoto IGN).
\nThe results of the mineralogical and geochemical characterization of the samples collected from tailings, soils, air, water, and watercourse sediments are presented and discussed here. Morphological evolution over time from Brunita and San Quintín mine ponds is presented too, as well as the geophysical study concerning the structure and infilling of ponds and the possible presence of acidic water flows.
\nThe mineralogical composition of tailings, colluvial, watercourse sediments, and soil samples has been inferred from the X-ray diffraction studies (Table 2). The following nomenclature has been used for tailing-mineral identification: primary minerals, those minerals that constitute ore and gangue assemblages originally deposited in the waste dumps, and secondary minerals, those deposited within the dumps by precipitation from metal-rich waters derived from acid mine drainage.
\nMine district | \nIberian Pyrite Belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n||||||
---|---|---|---|---|---|---|---|---|---|---|
Study area | \nLN | \nMR | \nMC | \nBR | \nSQ | \nSQ | \nSC | \nSC | \nLM | \nLM | \n
Sample | \nTa | \nTa | \nTa | \nTa | \nTa | \nCo | \nTa | \nWT | \nTa | \nWT | \n
Quartz | \n85 | \n35 | \n35 | \n30 | \n70 | \n80 | \n40 | \n45 | \n50 | \n40 | \n
Illite | \n— | \n10 | \n5 | \n5 | \n15 | \n10 | \n— | \n— | \n— | \n20 | \n
Feldspar | \n— | \n10 | \n5 | \n— | \n— | \n— | \n5 | \n30 | \n5 | \n5 | \n
Chlorite | \n5 | \n5 | \n5 | \n10 | \n10 | \n10 | \n— | \n— | \n5 | \n10 | \n
Calcite | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n20 | \n
Siderite | \n— | \n— | \n— | \n15 | \n— | \n— | \n— | \n— | \n— | \n— | \n
Pyrite | \n— | \n25 | \n35 | \n30 | \n— | \n— | \n10 | \n5 | \n15 | \n— | \n
Sphalerite | \n— | \n10 | \n5 | \n5 | \n— | \n— | \n5 | \n5 | \n10 | \n— | \n
Galena | \n— | \n— | \n— | \n— | \n— | \n— | \n10 | \n5 | \n5 | \n— | \n
Arsenopyrite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Magnetite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Jarosite | \n5 | \n5 | \n— | \n— | \n— | \n— | \n15 | \n5 | \n5 | \n— | \n
Rozenite | \n— | \n— | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Goethite | \n5 | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n
Hematite | \n— | \n— | \n— | \n— | \n— | \n— | \n5 | \n— | \n5 | \n— | \n
Alunite | \n— | \n— | \n— | \n— | \n— | \n— | \n— | \n5 | \n— | \n— | \n
Gypsum | \n— | \n— | \n5 | \n5 | \n5 | \n— | \n10 | \n— | \n— | \n5 | \n
Semi-quantitative mineralogical composition (wt%) of the studied samples.
La Naya (LN), Monterromero (MR), Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM). Ta: tailings; Co: colluvial; and WS: watercourse sediments.
The nearly homogeneous mineralogical composition of mine tailings is mainly composed of primary gangue minerals from the volcanic or metamorphic host rocks: quartz (30–85 wt%), illite (5–15 wt%), feldspar (5–10 wt%), and chlorite (5–10 wt%). Minor gangue minerals appear in important amounts in some of the areas: siderite (15 wt%) in Brunita. The most important feature of the mineralogical composition of these deposits is the metallic ore mineral contents (25–40 wt%). Significant amounts of pyrite (10–35 wt%), sphalerite (5–10 wt%), and/or galena (5–10 wt%) have been identified in mine tailings (Table 2). These high values are probably related to inefficient metallurgical processing of the benefited ore during the operational years. Because of the re-working of tailing mine areas, San Quintín area shows the lower ore mineral content. In other cases, like Brunita deposit, different ore minerals amounts are associated with the two different mines exploited and dumped: Brunita and Eloy mines. Cinnabar was identified by X-ray diffraction in one borehole sample from San Quintín. Its presence is due to the experimental metallurgical works carried out during the last period of operations in the Almadén mine (Ciudad Real, Spain). Secondary mineralogy is mainly represented by Fe-sulfates (jarosite and rozenite), Ca-sulfates (gypsum), and Al-sulfates (alunite). Fe-bearing sulfide oxidation increases the metal mobility from these materials compared to the levels mainly composed by sphalerite and galena. Significant amounts of secondary gypsum are typically found in this type of sulfide tailings. Fe-oxides and Fe-hydroxides have also been identified.
\nIn some occasions, the ore minerals are not identified by X-ray diffraction, or are identified in low amounts. In these cases, a detailed study by environmental scanning electron microscopy (ESEM) coupled with energy dispersive X-ray analysis (EDX) is necessary. Four examples of the application of ESEM-EDX are presented in Figure 2. Primary sulfide minerals (e.g. galena) identified in low amounts by XRD were also recognized by ESEM-EDX in Monte Romero tailings. Galena occurred as cubic crystals commonly showing octahedron faces. Other sulfide phases such as arsenopyrite, chalcopyrite, and galena were not detected by X-ray diffraction in La Naya tailings. Secondary mineral phases recognized by ESEM-EDX were Fe-oxyhydroxides. Cryptocrystalline Fe-oxyhydroxides frequently occurred around other minerals such as quartz, completely or partially replacing primary sulfides (pyrite and sphalerite). In San Quintín mine, primary ore minerals were not identified by XRD due to the optimized mining works. Pyrite, galena, chalcopyrite, and gangue minerals (barite) were identified by ESEM-EDX (Figure 2). In San Cristóbal tailings, ore and gangue minerals were identified, as well as oxide minerals such as goethite. In Las Moreras samples, low quantities of Fe-oxides (hematite and magnetite) and carbonates (siderite) were identified by ESEM.
\nBackscattered electron (BSE) images: (a) galena crystal from Monte Romero; (b) pyrite crystal from San Quintín; (c) altered faces of a pyrite crystal from San Cristóbal; (d) subidiomorphic magnetite from Las Moreras.
With respect to the colluvial sediments drilled at the San Quintín boreholes, the mineralogical composition was totally composed by primary silicates, as well as watercourse sediments from Las Moreras area (Table 2). The ore mineral content is low enough to be identified by XRD analysis. In contrast, the semi-quantitative mineralogical composition of watercourse sediment from San Cristóbal mine included ore minerals (pyrite, sphalerite, and galena) and secondary sulfates (jarosite and alunite).
\nTotal ferric iron (Fe2O3total), S, and trace element (Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn) concentrations, and pH values from tailing samples of the four mine district are summarized in Table 3. All samples showed a pH range of 2.2–5.6. This value range reflects the typical acid character of stored mine tailings. The composition of all tailing samples is characterized by the high contents of ore-bearing elements in each district: As, Cu, and Pb in the Iberian Pyrite Belt, Pb and Zn in Cartagena-La Unión and Alcudia Valley, and As, Pb, and Zn in Mazarrón. The total ferric iron content is significantly high in analyzed samples from all mine districts due to the omnipresence of Fe-bearing minerals like pyrite. The significantly high contents of potentially hazardous elements like Fe, Cu, Pb, and/or Zn are due to the nature of the mined ore, which is mainly composed of pyrite, chalcopyrite, sphalerite, and galena (Table 2). The highest metal contents are related to the mining history of each district, and the efficiency of the metallurgical processing in the benefited ore during the operational period of time. In the case of San Quintín mine, approximately 3 million tons of minerals from the tailings were re-worked. Then, the lowest Pb and Zn contents are located at the upper levels of the ponds. High Hg content measured in the San Quintín mine tailings is related to the experimental metallurgical works previously cited (Section 2). Significant Hg values were measured in Monte Romero mine related to the formation of a replacive mineralization. Pb (up to 21,130 μg g−1) and Zn (41,841 μg g−1) contents in the tailings from Mazarrón district (Table 3) as well as the significant Ag content from San Cristóbal mine related to the exploitation of Ag-bearing galena deserve special mention.
\nMine district | \nIberian Pyrite Belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|---|---|
Study area | \nLN | \nMR | \nMC | \nBR | \nSQ | \nSC | \nLM | \n
Ag | \nb.d. | \n31–81 (48) | \nb.d. | \n2–8 (4) | \n5–29 (12) | \n20- > 100 (55) | \n14–26 (20) | \n
As | \n191–909 (472) | \n1110–2740 (1650) | \n223–1080 (613) | \n39–385 (195) | \n16–54 (28) | \n400–633 (490) | \n181–630 (366) | \n
Au | \n49–100 (70) | \n311–744 (558) | \n22–86 (53) | \nn.a. | \nn.a. | \nn.a. | \nn.a. | \n
Cd | \nn.a. | \nn.a. | \nn.a. | \n3–67 (22) | \n5–22 (13) | \n4–833 (110) | \n97–373 (186) | \n
Cu | \n306–4511 (998) | \n914–16,582 (4874) | \nn.a. | \n76–323 (179) | \n42–381 (171) | \n121–882 (406) | \n168–356 (230) | \n
Fe2O3 total | \n8.51–14.1 (11.5) | \n2.12–27.2 (14.57) | \n6.11–24.1 (14) | \n22.3–52.6 (38.7) | \n4.5–6.3 (5.6) | \n9.8–22.9 (19.2) | \n17.7–28.5 (23.7) | \n
Ni | \nb.d. | \nb.d. | \nb.d. | \n11–40 (22) | \n29–61 (44) | \n11–41 (21) | \n41–59 (49) | \n
Pb | \n51–222 (133) | \n295–12,610 (4615) | \n71–475 (264) | \n1610–5950 (3211) | \n1510–10,500 (3992) | \n5987–39,877 (21,130) | \n3940–5239 (4665) | \n
S | \nn.a. | \nn.a. | \nn.a. | \n3.8–19.1 (11.8) | \n0.3–1.0 (0.4) | \n3.5–53.0 (6.6) | \n7.1–12.8 (9.3) | \n
Sb | \n31.3–50.4 (35.2) | \n168–861 (338) | \n16.2–67.5 (40) | \n3–54 (30) | \n36–162 (79) | \n108- > 200 (149) | \n54–136 (93) | \n
Sn | \nb.d. | \nb.d. | \nb.d. | \n14–244 (76) | \n3–7 (5) | \n8–121 (83) | \n24–37 (30) | \n
Zn | \n50–260 (143) | \n0.5–6.9 (4) | \n200–970 (511) | \n2020–2112,150 (6315) | \n1250–4470 (2418) | \n2500–11,405 (4101) | \n12,810–41,841 (27,738) | \n
pH | \n2.8–3.5 (3.2) | \n2.5–3.2 (3.0) | \nn.a. | \n2.4–3.7 (3.0) | \nn.a. | \n2.2–3.6 (2.5) | \n2.8–5.6 (4.0) | \n
Fe2O3 total, trace element content, and pH values in the studied tailings.
Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn in μg/g. Fe2O3total and S in wt%. Mean in brackets. b.d.: below detection; n.a.: not analyzed. La Naya (LN), Monterromero (MR), Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM).
The Mina Concepción samples were collected with a manual sampler from the first meter in depth. That is the reason for the lower metal contents to be associated with the more recent and efficient metallurgical works. Related to the Iberian Pyrite Belt district, relevant variations as a function of depth were identified in all of the analyzed element contents from Monte Romero samples. Possible explanations for these variations could be argued: (a) periods with higher mineral benefit, due to improvements in metallurgic processes or to a higher grade mineralogy and (b) a change in the exploitation targets, originally focused on galena (Pb) mining but later re-directed to pyrite (Fe) and sphalerite (Zn) mining due to environmental policies that do not recommend the use of lead in many industrial fields.
\nTotal ferric iron, S and trace element concentrations, and pH values from colluvial and watercourse sediment, and soil samples of the Alcudia Valley and Mazarrón districts are summarized in Table 4.
\nMine district | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|
Study mine | \nSan Quintín | \nSan Cristóbal | \nLas Moreras | \n||
Sample | \nColluvial | \nSoil | \nBlank | \nSediment | \nSediment | \n
Ag | \n0–3 (2) | \n0–3 (1) | \n0.2 | \n21–60 (38) | \n0–7 (3) | \n
As | \n8–24 (17) | \nb.d.-26 (15) | \n11 | \n216–312 (259) | \n15–131 (33) | \n
Cd | \n1–7 (3) | \nb.d.-7 (b.d.) | \nb.d. | \n2–4 (3) | \n1–6 (3) | \n
Cu | \n38–196 (76) | \n5–39 (17) | \n18 | \n70–122 (96) | \n45–482 (177) | \n
Fe2O3 total | \n5.3–9.5 (6.9) | \n1.7–4.7 (3.5) | \n4.2 | \n7.6–33.5 (18.3) | \n4.0–12.0 (5.9) | \n
Ni | \n46–93 (59) | \n13–46 (34) | \n30 | \n10–14 (12) | \n30–61 (47) | \n
Pb | \n79–577 (315) | \n41–1110 (318) | \n34 | \n9395–16,193 (12,800) | \n157–1880 (555) | \n
S | \n0–0.2 (0.1) | \n0–0.3 (0.1) | \n0.01 | \n2.7–4.5 (3.9) | \n0.1–3.0 (1.0) | \n
Sb | \n5–40 (24) | \n2–30 (9) | \n2 | \n98–124 (111) | \n2–48 (14) | \n
Sn | \n3–5 (4) | \n2–3 (3) | \n2 | \n5–10 (7) | \n2–6 (4) | \n
Zn | \n186–844 (503) | \n34–1180 (335) | \n49 | \n815–1660 (1334) | \n452–10,693 (2907) | \n
pH | \nn.a. | \n5.7–6.2 (5.9) | \n5.1 | \n3–3.4 (3.2) | \n7.0–7.7 (7.4) | \n
Fe2O3 total, trace element content, and pH values in the studied colluvial, soil, and watercourse sediment samples.
Ag, As, Au, Cd, Cu, Ni, Pb, Sb, Sn, and Zn in μg/g. Fe2O3 and S in wt%. Mean in brackets. b.d.: below detection. n.a.: not analyzed.
The highest contents were found in the pond samples, and the intermediate contents in the colluvial samples from San Quintín area. The ponds were not waterproofed, and hazardous metals from the upper ponds have percolated through the underlying colluvial sediments. Cu, Pb, and Zn contents show significant amounts. Five representative soil samples were analyzed in order to determine the importance of contamination (Table 4). Two mine soil samples show similar metal and As content to the upper tailing samples. The other two were agricultural soil samples, showing significantly lower metal and As content, but higher Hg and Pb contents than the local background sample (blank in Table 4), collected from an agricultural soil 4.5 km to the south-east. However, remarkably high As, Pb, and Zn contents were still found in this background sample, suggesting that the surrounding agricultural soils are also contaminated. In fact, Ag, Cd, Pb, and Zn contents from agricultural soil samples are higher than geochemical baselines reported by [22] for this Spanish region.
\nIn Mazarrón district, both tailings and watercourse sediments showed high amounts of potentially toxic elements, slightly lower at the sedimentary level (3.0–5.5 m depth). The total iron content ranged between 4.0 and 33.5 wt%, Pb was 157–16,193 μg g−1, and the Zn content ranged between 815 and 10,693 μg g−1 (Table 4). Other trace elements that displayed high values were: As, Cu, and Sb. The sediments mark a defined geochemical limit with the tailing unit. The upper mine tailings are significantly concentrated in Fe2O3total and heavy metals, whereas the sediments display marked lower values. This decrease is not absolutely regular, with major peaks in Cu and Zn contents, and minor increases in As, Cu, Fe2O3total, Pb, and Zn. The amount of calcite in the Las Moreras sedimentary unit (Table 2) controls the pH, buffering to within a small range of 7.2–7.7 (Table 4). In turn, the upper tailing unit of Las Moreras shows much lower pH values (2.8–5.6), due to the sulfide content and the complete absence of calcite.
\nThe composition of leakage sample waters of a restored mine pond (Mina Concepción) indicates that these waters represent acid mine drainage, as reflected by their very low pH (<2.6) (Table 5). Trace element contents are very high for Cu and Zn (>2 mg/l), both higher than the EPA’s maximum recommended limits for irrigation waters (0.2 mg/l for Cu and 2 mg/l for Zn [23]). This is also the case for As in samples leaking from the dyke wall and the puddle. Lead also goes beyond the legislation limits in samples from the dyke wall and the drainage pipes. Acid mine drainage was observed in the northern part of the Brunita mine pond (pH < 2.4) (Table 5). The concentrations were very high for Cu, Zn, Cd, Ni, and Fe in the water sample. Results from complementary techniques (ERT), shown in Section 4.3 of this chapter, have confirmed the formation of AMD waters in Mina Concepción and Brunita mines.
\nMine district | \nIberian pyrite belt | \nCartagena-La Unión | \nAlcudia Valley | \nMazarrón | \n|||
---|---|---|---|---|---|---|---|
Study mine | \nMC | \nBR | \nSQ | \nSC | \nLM | \n||
Sampling | \nLK (5) | \nWT | \nBO | \nWT (6) | \nAMD (3) | \nWT | \nWC | \n
As | \n>2000 | \n79 | \n2.6 | \n>1.5 | \n2.3 | \n>2000 | \nb.d. | \n
Cd | \n14–54 | \n483 | \n3.8 | \n0.1–26.6 | \n>3200 | \n6470 | \n1.5 | \n
Cu | \n>2000 | \n>2000 | \n12.3 | \n1–12.6 | \n>8300 | \n>2000 | \n52 | \n
Fe2O3 total | \n>100,000 | \n>100,000 | \n90 | \n30–100 | \n>189,000 | \n>100,000 | \n100 | \n
Ni | \n30–143 | \n559 | \n80.1 | \n1.8–22.3 | \n>3500 | \n5620 | \n120 | \n
Pb | \n0–67 | \n199 | \n8.5 | \n0.4–21.6 | \n>2800 | \n0.5 | \n1 | \n
Sb | \n0–1 | \nb.d. | \n6.1 | \n0.1–3.0 | \n1.3 | \n11.8 | \n1.4 | \n
Sn | \nb.d. | \nb.d. | \nb.d. | \nb.d. | \nb.d. | \n3 | \n2 | \n
Zn | \n>2500 | \n>2500 | \n93.6 | \n57–2520 | \n>550,000 | \n>2500 | \n365 | \n
pH | \n2–2.6 | \n2.4 | \n5.7 | \n6.2–7.1 | \n2.5–4.3 | \n1.8 | \n8.3 | \n
Fe2O3 total, trace element content, and pH values in the water samples.
Values in μg/L. b.d.: below detection. Mina Concepción (MC), Brunita (BR), San Quintín (SQ), San Cristóbal (SC), and Las Moreras (LM). LK: leakage; WT: water table; BO: borehole; AMD: acid mine drainage; and WC: watercourse.
One water sample was collected at 8 m depth in the borehole from San Quintín mine (Table 5). The high EC and acidic pH values are consistent with water from ore deposits retained in tailing ponds. Three samples showing low pH and significantly high trace element contents indicate AMD flowing from the remaining tailings. AMD was not observed in samples from the watercourse crossing the mining zone (Table 5). pH values in these waters are circumneutral, and EC values and metal contents are significantly lower than in samples from the tailings. Samples collected up- and downstream display the lowest trace element contents. Higher metal contents have been measured in the rest of watercourse samples, denoting that trace element contamination occurs through the mining area.
\nWith regard to the San Cristóbal mine pond, AMD was clearly detected in the water sample: pH < 2, high redox potential, high EC, and Total dissolved salt values. Concentrations of trace elements were very high for As and Cu (>2000 μg/L), Zn (>2500 μg/L), Cd and Ni (>5600 μg/L), and Fe (>100,000 μg/L). Water from the seasonal watercourse of Las Moreras was also analyzed. Significant contents of metallic elements (Cu, Fe, Ni, and Zn) were measured, all beyond the established limits for irrigation waters.
\nA singular case occurs in San Quintín mine where significantly high Hg content has been identified in the mine area. Gaseous Hg emissions were measured from the tailings and surrounding soils (Figure 3). The total gaseous mercury distribution in the studied area significantly changes between summer and winter. The area affected by TGM values up to 100 ng m−3 is restricted to the surroundings of the cinnabar stockpile in winter, but the affected area is 0.16 km2, and extends into the Don Quixote Route in summer. TGM values are lower than the limit recommended for the general population by the World Health Organization (WHO) (1000 ng m−3) for the worst scenario [24]: higher temperature and solar radiation during summer.
\nTotal gaseous mercury (TGM) seasonal distribution in the San Quintín area: (a) summer and (b) winter values. Modified from Martín-Crespo et al. [6].
Multivariate analysis has been carried out on the significant metal contents from samples of tailings (Brunita), tailings + colluvial sediments (San Quintín), and tailings + watercourse sediments + bedrock (San Cristóbal and Las Moreras) (Figure 4). Statistical data processing was carried out using Minitab® 16 software. The multivariate analysis was based on clustering (group average linkage dendrograms, Euclidean distance) of the set of samples and significant trace elements (Ag, As, Cd, Cu, Pb, Sb, Sn, and Zn). The dendrogram of the metals and As in the Brunita tailing samples shows the metallic signature of the district ores: Ag-Pb-Cd-Zn, Cu, and As-Sb-Sn, with As being mainly related to Sb (tetrahedrite-tenanntite mineral group). Ag-Pb-Cd-Zn signature is clearly defined due to the mineral source. In the case of San Quintín, the dendrogram from tailings and colluvial sediments reflects again the metallic signature of the district (Pb-Ag-Sb, Cu, and Zn-Cd to a certain extent [13]), with As mainly related to Sb (bournonite and boulangerite) and Pb-Ag (galena). Some samples display a strong affinity to the Ag-Pb-Sb-As association, whereas other samples display Cd-Zn affinity. The same metallic signature has been obtained from tailings and colluvial sediments, reflecting the same origin for both kinds of samples. The external origin of Hg is reflected by the highest obtained distances. The same occurs for the samples from Mazarrón district, reflecting the metallic signature of the district (Pb-Ag-Sb, Zn-Cd, and Cu to a certain extent [14]) with As being mainly related to the Sb (stibnite and sulfosalts) and Pb and Ag (galena). Some slight differences are displayed in the samples from sediments and bedrocks, particularly the larger range of Cu content.
\nDendrograms (distance Euclidean) of metals: (a) Brunita tailings; (b) San Quintín tailings and colluvial; (c) Mazarrón tailings; and (d) Mazarrón watercourse sediments and bedrocks.
All dendrograms presented in Figure 4 are in good agreement with field data, and mineralogical and geochemical features of tailings and watercourse deposits (Tables 2, 3, 4 respectively). In summary, the metallic signature of the three districts is clearly defined in the samples from tailings and affected sediments.
\nAdditional information about the characteristics of the mine tailing deposits can be obtained from electrical resistivity tomography (ERT) data. The major pieces of information that this method provides are related to both the thickness of the deposits and the occurrence of AMD (both inside the mine pond as flowing out through the dyke or the base). Figure 5 shows several examples of the type of information derived from the application of ERT to different mine ponds, resulting in a valuable tool that completes the information derived from mineralogical and geochemical techniques.
\nERT profiles obtained at four different mine sites. Each profile provides information about the thickness of the mine deposits and the presence or not of acidic water.
As a general rule, the materials that constitute the mine pond infilling are characterized by a medium to fine texture and high-water content. Moreover, due to oxidization of sulfide minerals, the pH of the water stored in the mine pond infilling is frequently acidic (pH < 5) in character. Opposite to this, the host rock where the mine pond is placed should be the host rock of the mineralization, typically metamorphic and/or igneous rocks of coarse texture and extremely low water content. Thus, a high resistivity contrast between the mine pond infilling (low to very low resistivity values) and the host rock (medium to high resistivity values) exists, allowing an accurate characterization of the boundary between both rock types and providing good estimations of the thickness of the mine pond deposits. Different mine pond thickness values and bottom geometries (dashed white lines) are imaged in Figure 5. Monte Romero and San Quintin mine ponds show simple bowl-shaped geometries with a thickness of ~3 and 10 m, respectively (confirmed with data from a borehole in the case of San Quintin mine pond 1), whereas Mina Concepcion and Brunite mine ponds exhibit a stepped bottom geometry with variable thickness (~6–10, and ~5–12 m, respectively). Where different rock units are present below the mine pond, instead of a homogeneous lithology, an estimation of the thickness of the different units can also be obtained. This is the case for San Quintin mine ponds, where a ~10 m thick sedimentary unit of colluvial deposits overlies the metasediments that constitute the regional basement. As mentioned before, a low pH value for the water contained in the mine pond deposits is also frequent, resulting in lower resistivity values in comparison with water with circumneutral pH. Therefore, the occurrence of acidic water inside a mine pond is revealed by extremely low resistivity values, normally lower than 1 ohm m. This is the case for Monte Romero, Mina Concepcion, and Brunita mine ponds where areas of <1 ohm m inside the mine pond correspond to the presence of water with pH ranging from 2 to 3 (see Table 5). On the other hand, the higher (>5 ohm m, and mainly >10 ohm m) resistivity values of the infilling of San Quintin mine ponds are associated with circumneutral pH (Table 5).
\nFinally, the strong resistivity contrast between the acidic water and the host rock results to be very useful to detect if AMD is flowing through the bottom of the mine pond. Where the sealing of the mine pond is correct, the host rock shows homogenous high resistivity values along the whole boundary with the pond infilling, such as the case of Monte Romero and San Quintin mine ponds. However, where AMD flows through the host rock, discrete areas of resistivity values much lower than the ones associated with the host rock are imaged, revealing the occurrence and sense of flow of the AMD. The latter is nicely imaged in both the cases of Mina Concepcion mine pond, where AMD flows from the inner central part of the pond toward the northern edge (confirmed during the field inspection of the dyke that exhibits AMD trough it), and Brunita mine pond, where AMD flows toward the east through the host rock.
\nDumping of huge piles of tailings and debris since the beginning of mining operations at the Brunita area leads to a deep transformation of the landscape with the whole disappearance of the original reliefs [5] (Figure 6). A few studies are recognized in the 1929 and 1946 aerial photographs, but mine ponds were not yet operational. The orography consisted of smooth hills (~40 height difference) separated by NNW–SSE and WNW-ESE ravines (<500 m long). However, by 1956, tailings from the mineral treatment plant were dumped in four stepped ponds along a NNW–SSE valley. Due to the continued mine activity, the tailings leveled the land up to the highest pond between 1973 and 1981, even burying the adjacent hills, and debris from the mine quarry began to cover the easternmost NNW–SSE ravines and hills. At this stage, there were two ponds, a large one with three stepped dikes and a small one in the highest part of the valley. The landslide scar in the dikes and the back sunken area, due to the flowage of tailings that caused the disastrous 1972 flash flood, are also visible. The sunken area was crisscrossed by gullies due to subsequent water erosion. Although the mine was closed in 1981, the reinforcement of the pond perimeter, with the sealing of the broken area, and the strong accumulation of debris to the east completely buried the ravines and hills and leveled the topography by 2004. Changes in the gully drainage pattern and the retreat of scarps indicate that tailing erosion persists.
\nLandscape evolution in the surroundings of the Brunita mine pond from aerial photographs taken in: 1929 (Ruiz de Alda photogrammetric flight); 1956 (Geographic Service of the Spanish Army); and 1981 and 2004 (Spanish National Geographic Institute). Blue lines, mine ponds in 1956; orange lines, mine ponds in 1981; brown dashed lines, badlands and landslide scar in 1981; and white line, spoil tips in 2004.
Eolian dispersion of contaminants from a tailing sand dune (Figure 1) is the most important environmental concern at the San Quintín area [21]. By 1977, the mine pond was divided into three sectors through dikes. One of these dikes will be the obstacle over which the dune will be developed when the mine operations ceased. The dune has been growing and migrating by the dominant winds since 1984. As the tailings are not replaced, the dune is losing its pollutant particles by dispersion toward the nearby river and agricultural soils, so its decrease in size or disappearance is expected.
\nThe results obtained from the mineralogical and geochemical characterization of the samples collected from tailings, soils, air, water, and watercourse sediments allow identifying the potential environmental concerns that would affect the different mine districts. These potential environmental concerns can be classified according to three main types: (a) ecosystem risks, (b) human health risks, and (c) physical hazards. Ecosystem risks are mainly related to the negative effect of both the acidic water and metals. To properly evaluate the potential volume of metals susceptible to produce negative effects on the ecosystems, the mineralogical and geochemical characterization of the tailings is crucial. From the cartographic (area) and ERT (general geometry and thickness) studies, an infilling volume of 912,000 m3 has been calculated for the Brunita mine pond. The maximum amounts of potential contaminants were obtained taking into account the mean content of potentially toxic elements (Table 3), the previously calculated volume, and the mass of the waste. A mean bulk density of 2.65 g/cm3 was calculated from the mineral particle density and assuming a porosity of 40%, which is the value for mine ponds originating from the processing of this type of deposit. From the mean trace element content shown in Table 3, the Brunita impoundment contains more than 24,250 t of potentially toxic elements such as (470 t), Cd (52 t), Cu (430 t), Ni (53 t), Pb (7753 t), Sb (71 t), Sn (184 t), and Zn (15,245 t). Release of these amounts of toxic elements would be catastrophic for the environment and the community (death, serious material damage, coastal areas, and farm land). Similar studies in the Iberian Pyrite Belt district show amounts of potentially toxic elements of 5900 t in La Naya, and 2100 t in Monte Romero ponds.
\nIn order to evaluate the contaminating degree of tailings, the geo-accumulation index (Igeo) was calculated. Müller [25] defined Igeo and enabled the assessment of sediment contamination by comparing current and pre-industrial concentrations of heavy metals. This index is mathematically expressed as Igeo = log2 Cn/1.5Bn, where Cn is the concentration of an element in the sample and Bn is the background concentration of the corresponding element in the Earth’s crust, according to [26]. Müller [25] suggested six descriptive classes for this index: uncontaminated (Igeo ≤ 0), uncontaminated to moderately contaminated (0 < Igeo < 1), moderately contaminated (1 < Igeo < 2), moderately to strongly contaminated (2 < Igeo < 3), strongly contaminated (3 < Igeo < 4), strongly to extremely contaminated (4 < Igeo < 5), and extremely contaminated (Igeo > 5). The Igeo index was calculated for tailings from Brunita, and tailings and colluvial from San Quintín (Figure 7). As, Cd, Pb, Sb, and Zn from Brunita tailings show extreme contamination (Igeo > 5), whereas Cu and Sn show moderate to strong contamination (1 < Igeo < 4). Ag is classified as a nonpollutant. The contamination classes are two levels higher than those obtained for similar tailings in Spain [6]. Cd, Hg, Pb, and Sb show extreme contamination (Igeo > 5), and As and Zn show moderate to heavy contamination (1 < Igeo < 5) in the tailings and colluvial sediment from San Quintín. Cu shows moderate to heavy contamination, and Ag is classified as unpolluted (Figure 7). Sutherland [27] proposed the enrichment factor (EF) to assess the level of contamination and the possible anthropogenic impact. To identify anomalous metal concentration, geochemical normalization of the heavy metal data to a conservative element, such as Fe, was employed (geochemical normalization). EF was calculated using the formula EF = (M/Fe)sample/(M/Fe)background, where (M/Fe)sample is the ratio of metal to Fe concentrations in the sample and (M/Fe)background is the ratio of metal to Fe concentrations of the background (blank; Table 4). Sutherland [27] proposed five contamination categories: minimal enrichment (EF < 2), moderate enrichment (2 < EF < 5), significant enrichment (5 < EF < 20), very high enrichment (20 < EF < 40), and extremely high enrichment (EF > 40). The San Quintín samples show very high to extremely high enrichment in Ag, Cd, Hg, Pb, Sb, and Zn. EF values for As are significantly lower than for the rest of elements, reflecting the lack of As-bearing minerals. Figure 8 shows Igeo and EF for San Quintín representative soil samples. Agricultural soil samples (S-06 and S-53) and the background sample (S-00) show the same features: they are moderately contaminated by As, Cd, Pb, and Sb and not contaminated by Ag, Cu, and Zn. The Igeo for Hg was strong for agricultural soils and extreme for mine soils. Agricultural soil samples (S-06; S-53) and mine soil sample (S-37) show minimal or moderate EF for Ag, As, Cd, Cu, Pb, Sb, and Zn. The EF values for Hg were significant or very high for agricultural soils and extremely high for mine soils. These data highlight the significant metal contents of the mine site, which can become especially hazardous due to eolian dispersion.
\n(a) Geoaccumulation index for Brunita tailings, (b) geoaccumulation index for San Quintín tailings and colluvial; (c) enrichment factor for San Quintín tailings and colluvial. Modified from Martín-Crespo et al. [5, 6].
Representative soils from San Quintín mine area: (a) geoaccumulation index and (b) enrichment factor. Modified from Martín-Crespo et al. [6].
The occurrence of AMD inside the tailings and its flow through the mine deposits toward the surrounding environment represents a major risk for the ecosystems. In this sense, several zones have been affected by metal mobilization though acidic water and its percolation from tailings to riverbed deposits, resulting in the affection of watercourses (Mina Concepcion) and groundwater (Brunita). Consequently, Mazarrón and Iberian Pyrite Belt districts show water metal contents beyond the EPA’s maximum recommended limits in irrigation waters. Where AMD is confined inside the mine tailings (Monte Romero and San Quintín), metal mobilization also occurs but the affection to the environment is limited. However, the large volume of acidic water with high metal contents stored at these deposits represents a major potential ecosystem risk. If a failure of the dam occurs, or the sealing of the mine pond fails, the ecosystem, watercourses, and riverbed sediments would be largely affected by the release of acidic water and its dissolved hazard metals.
\nRegarding human health risks, they are mainly associated with the eolian dispersion of contaminants. San Quintin mine ponds represent the area with the higher risk due to the combined effect of both the eolian dispersion of metals from the dune developed on the mine tailings, affecting the surrounding agricultural soils, and the gaseous mercury emissions. As previously mentioned in Section 4.2.2, agricultural soils surrounding San Quintín mine display As, Cd, Pb, and Zn contents higher than geochemical baseline. Therefore, they are contaminated and can be considered as a potential human health risk by the metal input to the olive tree crops. Nevertheless, metal contents in water from the watercourse crossing the mining area are below recommended limits for irrigation waters, denoting not significant affection by AMD. Although this zone is not remediated and not in a condition for public transit, the San Quintín mine has been reported as one of the points to be visited on the longest Eco-tourist Itinerary in Europe, named “Don Quixote Route, a place for adventure”. Section four of the route crosses the San Quintín mining area, exhibiting ruinous mine structures. This mine has become a representative example of the socio-economic and cultural benefits that its restoration could confer to this zone. Although these types of tourist initiatives are remarkable in terms of geological heritage, a previous characterization and reclamation study has not been carried out.
\nPhysical hazards are mainly related to the presence of open shafts and unstable ponds and have also been identified in the different mine districts. Alcudia valley and Mazarrón districts contain many abandoned open shafts and tunnels. Unstable ponds have also been identified as in the case of Brunite mine pond, where a previous dam failure occurred [5]. Similar to this, the outflow of acidic water through the dam of Mina Concepcion mine pond would represent a source of instability resulting in a potential physical hazard.
\nThis work revealed that the joint use of mineralogical, geochemical, and geophysical techniques can provide an environmental characterization of abandoned mine sites, allowing for estimations of potential pollution and the extent of affected zones.
\nSignificant potentially hazardous element contents have been identified in all studied mine districts, not only in the mine tailings but also in the underlying colluvial and alluvial sediments and surrounding soils. Mineralogical and geochemical signatures of the ore mineralization are clearly recognized in all analyzed samples. Pyrite, sphalerite, and galena are the main ore minerals identified in the mine tailings. Gangue minerals (quartz, illite, feldspar, and chlorite) and secondary minerals (Fe-sulfates, gypsum, and Fe-sulfates) have also been identified by XRD and/or ESEM-EDX. Significantly high contents of As, Cu, Pb, and Zn have been identified in the majority of the mine tailings, reflecting the related environmental hazards associated with all of these abandoned deposits. Moreover, significant potentially toxic element content has been analyzed in tailings from restored mine pond like Mina Concepción. Agricultural soil samples show lower metal and As content but higher Hg and Pb content than in the background sample in the San Quintín area. AMD has been clearly identified not only flowing from the remaining tailings, but also from a restored mine pond, denoting that environmental hazard persists.
\nERT provides valuable additional information about the mine deposits. The strong resistivity contrast between the infilling and the underlying rock allows obtaining both the thickness of the infilling as the geometry of the bottom mine pond. Moreover, if the infilling deposits contain water, the resistivity values provide information about both the acidic character of the water and the occurrence or not of AMD flow outside the mine pond. The mapping of mine deposits from time series of aerial images reveals the strong impact of mining on the landscape due to the dumping of large amounts of polluting wastes and their mobilization thereof to the surrounding areas by several geological processes (mass movement, gully erosion, and eolian dispersion).
\nMajor environmental hazards are associated with different main pathways (wind erosion and water flows) and several receptors (bathing waters, agricultural soils, humans, and sediments) depending on the specific mine area. In summary, this type of abandoned deposits need to be characterized, monitored, and restored in order to avoid mobilization of tens thousands of tons of potentially hazardous elements.
\nThis work has been accomplished on the frame of projects URJC-RNT-063-1, URJC-CM-2006-CET-0636, and URJC-CM-2008-CET-3644 funded by Comunidad de Madrid and Universidad Rey Juan Carlos. The Open Access Publishing Fee has been funded by the Universidad Rey Juan Carlos.
\nThe authors declare no conflict of interest in this chapter.
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