Effect of Long-Term Nutrient Management Strategies for Pastures on Phosphorus in Surface Runoff and Soil Quality

Manure, whether mechanically applied or deposited by grazing animals, has been associated with increased risk of non-point source pollution, especially phosphorus. This is especially true in areas where the industry, especially poultry, has been concentrated in geographical areas that are grain deficient, resulting in a reliance on imported grain for poultry feed. Intensification has resulted in the production of large quantities of poultry manure, within relatively small geographical areas. Surplus litter is typically land applied as a nutrient source or used as an animal feed. The objective of this project was to evaluate the effects of long-term nutrient management strategies using poultry litter as a feed and fertiliser for grazed pasture systems in the Shenandoah Valley of Virginia on soil quality, selected soil chemical characteristics and P losses in surface runoff

Grazing Behaviour of Beef Steers Grazing Kentucky 31 Endophyte Infected Tall Fescue, Q4508-AR542 Novel Endophyte Tall Fescue, and Lakota Prairie Grass

Tall fescue is the most dominant grass used for pasture in the U.S. covering over 14 million ha. As a result, fescue toxicosis is a major concern among producers, especially during the summer months when the symptoms, such as reduced weight gains, are most pronounced. Producers need alternative forages for grazing cattle that do not have the negative effects associated with endophyte infected tall fescue. The objective of this experiment was to determine the grazing behaviour of cattle on Kentucky 31 endophyte infected (E+) tall fescue (Festuca arundinacea Schreb.), Q4508-AR542 (Q) novel endophyte tall fescue, and Lakota (L) prairie grass (Bromus catharticus)

Use of Alkanes to Estimate Dry Matter Intake of Beef Steers Grazing High Quality Pastures

Pastures remain the most important source of nutrients for ruminant livestock and nutrition is critical to optimize animal production. The daily quantity of dry matter that is consumed by an animal is a critical measurement to make nutritional inferences about feed and subsequent animal response. Researchers are facing the dilemma that, while estimates of individual animal performance are readily obtained, it is still difficult to estimate the herbage intake of individual animals. The objectives of this experiment were to estimate forage intake in beef steers grazing tall fescue (Festuca arundinacea Schreb.) and alfalfa (Medicago sativa)/tall fescue pastures and to measure the recovery rate of artificial alkanes from a controlled release device under these conditions

Performance and Carcass Characteristics of Feedlot Steers: Effects of Delayed Implanting and Programmed Feeding During the Growing Period

This experiment was conducted to determine the effect of programming the rate of gain and delaying the first implant in feedlot steers on feedlot performance and carcass characteristics. Ninety-six growing steers (269 ± 16.2 kg) were assigned to 12 pens in a completely randomized design. Treatments were implant (Synovex-S®; 20 mg estradiol benzoate and 200 mg progesterone; Fort Dodge Animal Health, Overland Park, KS) on d 1 or no implant and programmed feeding to gain at a slow (0.68 kg/d) or fast (1.14 kg/d) rate during the growing period; these treatments were randomly assigned (n = 8) to pens of steers in a 2 × 2 factorial arrangement. Steers were fed a growing diet and after 88 and 60 d (for steers fed to gain at a slow or fast rate, respectively), steers were transitioned to ad libitum consumption of a high concentrate finishing diet. Growing period implant treatments did not affect ADG but did affect (P\u3c0.01) gain efficiency during the finishing period. Feeding steers for a slow rate of BW gain during the growing period improved (P=0.062) gain efficiency in the finishing period (169 vs 145 g gain/kg feed). Correlation coefficients between fat thickness and marbling score obtained via ultrasound and fat thickness and marbling score measured at harvest were greater the closer the ultrasound measurements were made to the final harvest date. These data indicate that feeding level prior to the start of the finishing period may affect BW gain efficiency during the finishing period

Parameters optimization applying Monte Carlo methods and Evolutionary Algorithms. Enforcement to a trajectory tracking controller in non-linear systems.

[EN] In this work, a closed-loop control strategy is proposed. It allows tracking optimal profiles for a fed-batch bioprocess. The main advantage of this approach is that the control actions are computed from a linear equations system without linearizing the mathematical model, which allows working in any range. In addition, three techniques are developed to tune the controller. First, a completely probabilistic method, Monte Carlo. Second, a methodology based on Genetic Algorithms, an evolutionary optimization technique. Third, a Hybrid Algorithm, combining above algorithms advantages. Here, the objective function is to find the controller parameters that minimize the trajectory tracking total error. The controller performance is evaluated through simulations under normal operations conditions and parametric uncertainty, using the obtained controller parameters.[ES] En este trabajo se propone una estrategia de control en lazo cerrado para el seguimiento de perfiles óptimos previamente definidos para un bioproceso fed-batch. La mayor ventaja de este enfoque es que las acciones de control se calculan resolviendo un sistema de ecuaciones lineales, sin tener que linealizar el modelo matemático, lo que permite trabajar en cualquier rango. Además, se plantean tres técnicas para la sintonización de los parámetros del controlador diseñado. Primero se propone un método de Monte Carlo, el cual es un método probabilístico. En segundo lugar, se presenta una metodología basada en Algoritmos Genéticos, una técnica evolutiva de optimización. La tercera alternativa es el desarrollo de un Algoritmo Híbrido, diseñado a partir de la combinación de los dos métodos anteriores. En todos los casos, el objetivo es encontrar los parámetros del controlador que minimicen el error total de seguimiento de trayectorias. El desempeño del controlador se evalúa a través de simulaciones en condiciones normales de operación y frente a incertidumbre paramétrica, empleando los parámetros del controlador obtenidos.Este trabajo ha sido realizado con el apoyo del Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) y del Instituto de Ingeniería Química (IIQ) de la Universidad Nacional de San Juan. Se agradece la colaboración del Dr. Ing. Francisco Rossomando en la implementación del controlador PID neuronal.Fernández, C.; Pantano, N.; Godoy, S.; Serrano, E.; Scaglia, G. (2018). Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales. Revista Iberoamericana de Automática e Informática. 16(1):89-99. doi:10.4995/riai.2018.8796SWORD899916

Linear Algebra Based trajectory control

[ES] En este tutorial se resumen las principales características de una nueva metodología de diseño de sistemas de control para el seguimiento de trayectorias en procesos no lineales. Esta metodología, denominada LAB (Linear Algebra Based), fue presentada por los autores hace más de diez años y ha tenido una fuerte repercusión por su sencillez y facilidad de aplicación, si bien no es aplicable para algunos problemas de seguimiento en sistemas no lineales. Se exponen las etapas en el diseño de un controlador LAB, tanto en tiempo continuo como en discreto. La aplicación al control de la trayectoria de un robot móvil, en tiempo continuo, sirve para ilustrar el desarrollo e implementación del control. Se analizan algunas propiedades del sistema controlado y se resaltan las condiciones de aplicación. Numerosas referencias facilitan el desarrollo de algunas características y su aplicación en diversos campos de la robótica y del control de procesos en general.[EN] In this tutorial, the main features of a new control design methodology for tracking control in nonlinear processes is summarized. The so called LAB (Linear Algebra Based) methodology was introduced by the authors more than ten years ago and it has been accepted and used by many researchers mainly due to its simplicity and easy application. Nevertheless, it is not applicable to all the tracking problems dealing with nonlinear systems. The LAB controller design procedure, both in continuous time and discretetime, is outlined. The design of the trajectory control of a mobile robot illustrates the procedure as well as its implementation. Some properties of the controlled process are discussed and the problem requirements for a successful application are pointed out. Several references allow a deeper analysis of the controlled plant features as well as its application in a variety of processes, either in robotics or in process control.Scaglia, GJE.; Serrano, ME.; Albertos Pérez, P. (2020). Control de trayectorias basado en álgebra lineal. Revista Iberoamericana de Automática e Informática industrial. 17(4):344-353. https://doi.org/10.4995/riai.2020.13584OJS344353174Apostol, T., 1967. CALCULUS, One -Variable Calculus, with an introduction to Linear Algebra. Blaisdell Publishing Company.Battilotti, S., Califano, C., 2004. A constructive condition for dynamic feedback linearization. Systems & control letters 52(5), 329-338. https://doi.org/10.1016/j.sysconle.2004.02.009Bouhenchir, H., Cabassud, M., Le Lann, M.-V., 2006. Predictive functional control for the temperature control of a chemical batch reactor. Computers & Chemical Engineering 30 (6-7), 1141-1154. https://doi.org/10.1016/j.compchemeng.2006.02.014Brockett, R., 1965. Poles, zeros, and feedback: State space interpretation. IEEE Transactions on Automatic Control 10(2), 129-135. https://doi.org/10.1109/TAC.1965.1098118Charlet, B., Levine, J., Marino, R., 1988. Dynamic feedback linearization with application to aircraft control. Proceedings of the 27th IEEE Conference on Decision and Control, Austin, TX, USA 1, 701-705.Chwa, D., 2004. Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE transactions on control systems technology 12 (4), 637-644. https://doi.org/10.1109/TCST.2004.824953den Boom, T. J. J. V., 1998. On feedback linearization in LMI-based nonlinear MPC. In Proceedings of the 1998 American Control Conference 3, 1684-1688.Devasia, S., Chen, D., B., P., 1996. Nonlinear inversion-based output tracking. IEEE Transactions on Automatic Control 41(7), 930-942. https://doi.org/10.1109/9.508898Fernandez, M. C., Romoli, S., Pantano, M. N., Ortiz, O. A., Patiño, D., Scaglia,G. J., 2018. A new approach for nonlinear multivariable fed-batch bioprocess trajectory tracking control. Automatic Control and Computer Sciences 52 (1), 13-24. https://doi.org/10.3103/S0146411618010030Francis, B. A., 1977. The linear multivariable regulator problem. SIAM Journal on Control and Optimization 15(3), 486-505. https://doi.org/10.1137/0315033Fukao, T., Nakagawa, H., Adachi, N., 2000. Adaptive tracking control of a nonholonomic mobile robot. IEEE transactions on Robotics and Automation 16 (5), 609-615. https://doi.org/10.1109/70.880812Gandolfo, D., Rosales, C., Patiño, D., Scaglia, G., Jordan, M., 2014. Trajectory tracking control of a pvtol aircraft based on linear algebra theory. Asian Journal of Control 16 (6), 1849-1858.https://doi.org/10.1002/asjc.819Ghandan, R., Blankenship, G. L., 1993. Adaptive approximate tracking and regulation of nonlinear systems. Proceedings of 32nd IEEE Conference on Decision and Control 1, 2654-2659.Hepburn, J., Wonham, W., 1984. Error feedback and internal models on dierentiable manifolds. IEEE Transactions on Automatic Control 29(5), 397-403. https://doi.org/10.1109/TAC.1984.1103563Huang, R., Zhu, J. J., 2009. Time-varying high-gain trajectory linearization observer design. Proceedings of American Control Conference 1, 4628-4635. https://doi.org/10.1109/ACC.2009.5160252Isidori, A., Byrnes, C. I., 1990. Output regulation of nonlinear systems. IEEE transactions on Automatic Control, 35(2), 131-140. https://doi.org/10.1109/9.45168Kanayama, Y., Kimura, Y., Miyazaki, F., Noguchi, T., 1990. A stable tracking control method for an autonomous mobile robot. In: Proceedings. IEEE International Conference on Robotics and Automation. IEEE, pp. 384-389.Khalil, H., 2002. Nonlinear Systems. Prentice Hall.Lee, H. G., Arapostathis, A., I.Marcus, S., 2003. An algorithm for linearization of discrete-time systems via restricted dynamic feedback. In Proceedings of 42nd IEEE International Conference on Decision and Control 2, 1362-1367.Levine, J., Marino, R., 1990. On dynamic feedback linearization in r/sup 4. In Proceedings 29th IEEE Conference on Decision and Control IEEE. Honolulu, Hawaii. 1, 2088-2090. https://doi.org/10.1109/CDC.1990.203992Li, X. S., Li, Y. H., Li, X., Peng, J., Li, C. X., 2012. Robust trajectory linearization control design for unmanned aerial vehicle path following. Systems Engineering and Electronics 34(4), 767-772.Li, Z., Deng, J., Lu, R., Xu, Y., Bai, J., Su, C.-Y., 2015. Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems 46 (6), 740-749. https://doi.org/10.1109/TSMC.2015.2465352Lustosa, L. R., Defaÿ, F., Moschetta, J. M., 2017. The feasibility issue in trajectory tracking by means of regions-of-attraction-based gain scheduling. IFAC-PapersOnLine 50(1), 11504-11508. https://doi.org/10.1016/j.ifacol.2017.08.1609Moore, J., Cory, R., Tedrake, R., 2014. Robust post-stall perching with a simple fixed-wing glider using LQR-Trees. Bioinspiration & biomimetics 9(2), 025013. https://doi.org/10.1088/1748-3182/9/2/025013Panahandeh, P., Alipour, K., Tarvirdizadeh, B., Hadi, A., 2019. A kinematic lyapunov-based controller to posture stabilization of wheeled mobile robots. Mechanical Systems and Signal Processing 134, 106319. https://doi.org/10.1016/j.ymssp.2019.106319Pantano, M. N., Fernandez, M. C., Serrano, M. E., Ortiz, O. A., Scaglia, G. J., 2018. Tracking control of optimal profiles in a nonlinear fed-catch bioprocess under parametric uncertainty and process disturbances. Industrial & Engineering Chemistry Research 57 (32), 11130-11140. https://doi.org/10.1021/acs.iecr.8b01791Pantano, M. N., Fernández, M. C., Serrano, M. E., Ortíz, O. A., Scaglia, G. J. E., 2019. Trajectory tracking controller for a nonlinear fed-batch bioprocess. Revista Ingeniería Electrónica, Automática y Comunicaciones ISSN:1815-5928 38 (1), 78.Proaño, P., Capito, L., Rosales, A., Camacho, O., 2015. Sliding mode control:Implementation like pid for trajectory-tracking for mobile robots. In: 2015 Asia-Pacific Conference on Computer Aided System Engineering. IEEE, pp.220-225. https://doi.org/10.1109/APCASE.2015.46Rojas, O. J., Goodwin, G. C., 2001. Preliminary analysis of a nonlinear control scheme related to feedback linearization. In Proceedings of the 40th IEEE Conference on Decision and Control 2, 1743-1748.Rosales, A., Scaglia, G., Mut, V., di Sciascio, F., 2009. Navegación de robots móviles en entornos no estructurados utilizando álgebra lineal. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(2), 79-88. https://doi.org/10.1016/S1697-7912(09)70096-2Rosales, C., Gandolfo, D., Scaglia, G., Jordan, M., Carelli, R., 2015. Trajectory tracking of a mini four-rotor helicopter in dynamic environments-a linear algebra approach. Robotica 33 (8), 1628-1652. https://doi.org/10.1017/S0263574714000952Scaglia, G., Montoya, L. Q., Mut, V., di Sciascio, F., 2009. Numerical methods based controller design for mobile robots. Robotica 27 (2), 269-279. https://doi.org/10.1017/S0263574708004669Scaglia, G., Quintero, O. L., Mut, V., di Sciascio, F., 2008. Numerical methods based controller design for mobile robots. IFAC Proceedings Volumes 41 (2), 4820 - 4827. https://doi.org/10.3182/20080706-5-KR-1001.00810Scaglia, G., Serrano, E., Rosales, A., Albertos, P., 2015. Linear interpolation based controller design for trajectory tracking under uncertainties: Application to mobile robots. Control Engineering Practice 45, 123-132. https://doi.org/10.1016/j.conengprac.2015.09.010Scaglia, G., Serrano, E., Rosales, A., Albertos, P., 2019. Tracking control design in nonlinear multivariable systems: Robotic applications. Mathematical Problems in Engineering 2019. https://doi.org/10.1155/2019/8643515Scaglia, G., Serrano, M., Albertos, P., 2020. Linear Algebra Based Controllers: Design and Applications. Springer International Publishing. URL: https://books.google.es/books?id=ELzoDwAAQBAJ , https://doi.org/10.1007/978-3-030-42818-1Serrano, M. E., Godoy, S. A., Quintero, L., Scaglia, G. J., 2017. Interpolation based controller for trajectory tracking in mobile robots. Journal of Intelligent & Robotic Systems 86 (3-4), 569-581. https://doi.org/10.1007/s10846-016-0422-4Serrano, M. E., Scaglia, G. J., Godoy, S. A., Mut, V., Ortiz, O. A., 2013. Trajectory tracking of underactuated surface vessels: A linear algebra approach. IEEE Transactions on Control Systems Technology 22 (3), 1103-1111. https://doi.org/10.1109/TCST.2013.2271505Silverman, L., 1968. Properties and application of inverse systems. IEEE transactions on Automatic Control 13(4), 436-437. https://doi.org/10.1109/TAC.1968.1098943Silverman, L., 1969. Inversion of multivariable linear systems. IEEE transactions on Automatic Control 14(3), 270-276. https://doi.org/10.1109/TAC.1969.1099169Sun, W., Tang, S., Gao, H., Zhao, J., 2016. Two time-scale tracking control of nonholonomic wheeled mobile robots. IEEE Transactions on Control Systems Technology 24 (6), 2059-2069. https://doi.org/10.1109/TCST.2016.2519282Xingling, S., Honglun, W., 2016. Trajectory linearization control based output tracking method for nonlinear uncertain system using linear extended state observer. Asian Journal of Control 18(1), 316-327. https://doi.org/10.1002/asjc.1053Zeng, G., Hunt, L. R., 2000. Stable inversion for nonlinear discrete-time systems. IEEE Transactions on Automatic Control 45(6), 1216-1220. https://doi.org/10.1109/9.863610Zhu, J. J., Banker, B., Hall, C., 2000. X-33 ascent flight control design by trajectory linearization-a singular perturbation approach. Proceedings of AIAA guidance, navigation, and control conference and exhibit 1, 4159. https://doi.org/10.2514/6.2000-4159Zhu, J. J., Funston, K., Hall, C. E., Hodel, A. S., 2001. X-33 entry flight control design by trajectory linearization- a singular perturbation approach. Guidanceand control 1, 151-170. https://doi.org/10.2514/6.2000-4159Zhu, L., Jiang, C. S., Xue, Y. L., 2008. Robust adaptive trajectory linearization control for aerospace vehicle using single hidden layer neutral networks. Acta Armamentarii 29(1), 52-56

Year-Round Forage Systems for Beef Cows and Calves

Beef cow systems in the USA are based on forages with little or no concentrates fed. Tall fescue (Festuca arundinacea Schreb. L.) is one of the important pasture forages in the lower Northeast and upper South (Allen et al., 2001). Limited research has been conducted on year-round all forage systems based on cool season forages. Stockpiling tall fescue in late summer-early fall provides good quality forage that is usually grazed rather than harvested. Forage systems including tall fescue and clover (Trifolium repens L.) produced excellent performance in beef cows and calves, with minimum inputs (Allen et al., 2001). The present experiment is a component of a larger initiative, Pasture-based Forage Systems for Appalachia. The specific objective of this experiment is to evaluate different forage systems for beef cows and calves

Dynamic optimization based on Fourier. Application to the biodiesel process

[EN] This work presents a novel methodology for the dynamic optimization of the biodiesel production process from vegetable oils in discontinuous mode. The proposed methodology has the particularity of using the Fourier series for the parameterization of the control action, and evolutionary algorithms for the optimization of parameters. The main advantages of this strategy are, on the one hand, that the profiles obtained are smooth, that is, continuous and differentiable, therefore they can be directly implemented in real systems, without the need to filter or soften the control signal; on the other hand, a minimum amount of parameters is required for optimization, avoiding over-parameterization, which can decrease the quality of the response. The proposed algorithms have been evaluated through simulations, obtaining very satisfactory results compared to those published in the literature.[ES] Este trabajo presenta una novedosa metodología para la optimización dinámica del proceso de producción de biodiesel a partir de aceites vegetales en modo discontinuo. La metodología propuesta tiene la particularidad de emplear la serie de Fourier para la parametrización de la acción de control, y algoritmos evolutivos para la optimización de parámetros. Las ventajas principales de esta estrategia son, por un lado, que los perfiles obtenidos son suaves, es decir, continuos y diferenciables, por lo tanto pueden implementarse directamente en sistemas reales, sin necesidad de filtrar o suavizar la señal de control; por otro lado, se requiere una mínima cantidad de parámetros para la optimización, evitando la sobre-parametrización, la cual puede disminuir la calidad de la respuesta. Los algoritmos propuestos han sido evaluados a través de simulaciones, obteniendo resultados muy satisfactorios comparados con los existentes en bibliografía.Agradecemos al Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET) por financiar este proyecto, y al Instituto de Ingeniería Química (IIQ) de la Universidad Nacional de San Juan (UNSJ) por su continua colaboración.Pantano, MN.; Fernández, MC.; Rodríguez, L.; Scaglia, GJ. (2020). Optimización dinámica basada en Fourier. Aplicación al proceso de biodiesel. Revista Iberoamericana de Automática e Informática industrial. 18(1):32-38. https://doi.org/10.4995/riai.2020.12920OJS3238181Benavides, P. T. & Diwekar, U., 2012a. Optimal control of biodiesel production in a batch reactor: Part I: Deterministic control. Fuel,94, 211- 217. https://doi.org/10.1016/j.fuel.2011.08.035Benavides, P. T. & Diwekar, U., 2012b. Optimal control of biodiesel production in a batch reactor: Part II: Stochastic control. Fuel,94, 218-226. https://doi.org/10.1016/j.fuel.2011.08.033Brásio, A. S., Romanenko, A., Leal, J., Santos, L. O. & Fernandes, N. C., 2013. Nonlinear model predictive control of biodiesel production via transesterification of used vegetable oils. Journal of Process Control,10,23, 1471-1479. https://doi.org/10.1016/j.jprocont.2013.09.023Cantrell, D. G., Gillie, L. J., Lee, A. F. & Wilson, K., 2005. Structure-reactivity correlations in MgAl hydrotalcite catalysts for biodiesel synthesis. Applied Catalysis A: General,2,287, 183-190. https://doi.org/10.1016/j.apcata.2005.03.027Fernández, Cecilia, M., Nadia Pantano, M., Rossomando, F. G., Alberto Ortiz, O. & Scaglia, G. J., 2019. State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system. Brazilian Journal of Chemical Engineering,1,36, 421-437. https://doi.org/10.1590/0104-6632.20190361s20170379Fernández, C., Pantano, N., Godoy, S., Serrano, E. & Scaglia, G., 2019a. Optimización de Parámetros Utilizando los Métodos de Monte Carlo y Algoritmos Evolutivos. Aplicación a un Controlador de Seguimiento de Trayectoria en Sistemas no Lineales. Revista Iberoamericana de Automática e Informática industrial,1,16, 89-99. https://doi.org/10.4995/riai.2018.8796Fernández M. C., P. M. N., Rodriguez L., Scaglia G., 2020. State Estimation and Nonlinear Tracking Control Simulation Approach. Application to a Bioethanol Production System. Bioprocess and Biosystems Engineering,In press.Fernández, M. C., Pantano, M. N., Machado, R. A. F., Ortiz, O. A. & Scaglia, G. J., 2019b. Nonlinear multivariable tracking control: application to an ethanol process. International Journal of Automation and Control,4,13, 440-468. https://doi.org/10.1504/IJAAC.2019.10020240Fernández, M. C., Pantano, M. N., Rómoli, S., Patiño, H. D., Ortiz, O. A. & Scaglia, G. J., 2019c. An algebra approach for nonlinear multivariable fedbatch bioprocess control. International Journal of Industrial and Systems Engineering,1,33, 38-57. https://doi.org/10.1504/IJISE.2019.10023564Fernández, M. C., Pantano, M. N., Serrano, E. & Scaglia, G., 2020. Multivariable Tracking Control of a Bioethanol Process under Uncertainties. Mathematical Problems in Engineering,2020. https://doi.org/10.1155/2020/8263690Ho, Y., Mjalli, F. & Yeoh, H., 2010. Multivariable adaptive predictive model based control of a biodiesel transesterification reactor. Journal of Applied Sciences,12,10, 1019-1027. https://doi.org/10.3923/jas.2010.1019.1027Ignat, R. M. & Kiss, A. A., 2013. Optimal design, dynamics and control of a reactive DWC for biodiesel production. Chemical Engineering Research and Design,9,91, 1760-1767. https://doi.org/10.1016/j.cherd.2013.02.009Kreyszig, E. 1978. Introductory functional analysis with applications, Wiley New York.Mjalli, F. S., Kim San, L., Chai Yin, K. & Azlan Hussain, M., 2009. Dynamics and control of a biodiesel transesterification reactor. Chemical Engineering & Technology,1,32, 13-26. https://doi.org/10.1002/ceat.200800243Nagle, R. K., Saff, E. B. & Snider, A. D. 2001. Ecuaciones diferenciales y problemas con valores en la frontera, Pearson Educación.Nasir, N., Daud, W. R. W., Kamarudin, S. & Yaakob, Z., 2013. Process system engineering in biodiesel production: A review. Renewable and Sustainable Energy Reviews,22, 631-639. https://doi.org/10.1016/j.rser.2013.01.036Nearing, J., 2006. Mathematical tools for physics.Pantano, M. N., Fernández, M. C., Serrano, M. E., Ortiz, O. A. & Scaglia, G. J. E., 2018. Tracking Control of Optimal Profiles in a Nonlinear Fed-Batch Bioprocess under Parametric Uncertainty and Process Disturbances. Industrial & Engineering Chemistry Research,32,57, 11130-11140.https://doi.org/10.1021/acs.iecr.8b01791Pantano, M. N., Serrano, M. E., Fernández, M. C., Rossomando, F. G., Ortiz, O. A. & Scaglia, G. J., 2017. Multivariable Control for Tracking Optimal Profiles in a Nonlinear Fed-Batch Bioprocess Integrated with State Estimation. Industrial & Engineering Chemistry Research,20,56, 6043- 6056. https://doi.org/10.1021/acs.iecr.7b00831Rajarathinam, K., Gomm, J. B., Yu, D.-L. & Abdelhadi, A. S., 2016. PID controller tuning for a multivariable glass furnace process by genetic algorithm. International Journal of Automation and Computing,1,13, 64- 72. https://doi.org/10.1007/s11633-015-0910-1Salvi, B. & Panwar, N., 2012. Biodiesel resources and production technologies-A review. Renewable and Sustainable Energy Reviews,6,16, 3680-3689. https://doi.org/10.1016/j.rser.2012.03.050Santori, G., Di Nicola, G., Moglie, M. & Polonara, F., 2012. A review analyzing the industrial biodiesel production practice starting from vegetable oil refining. Applied energy,92, 109-132. https://doi.org/10.1016/j.apenergy.2011.10.031Tempo, R. & Ishii, H., 2007. Monte Carlo and Las Vegas Randomized Algorithms for Systems and Control: An Introduction. European Journal of Control,2-3,13, 189-203. https://doi.org/10.3166/ejc.13.189-203Wali, W., Al-Shamma'a, A., Hassan, K. H. & Cullen, J., 2012. Online geneticANFIS temperature control for advanced microwave biodiesel reactor. Journal of Process Control,7,22, 1256-1272. https://doi.org/10.1016/j.jprocont.2012.05.013Wali, W., Hassan, K., Cullen, J., Shaw, A. & Al-Shamma'a, A., 2013. Real time monitoring and intelligent control for novel advanced microwave biodiesel reactor. Measurement,1,46, 823-839. https://doi.org/10.1016/j.measurement.2012.10.004Zhang, M., Gao, Z., Zheng, T., Ma, Y., Wang, Q., Gao, M. & Sun, X., 2016. A bibliometric analysis of biodiesel research during 1991-2015. Journal of Material Cycles and Waste Management, 1-9. https://doi.org/10.1007/s10163-016-0575-zZhang, Y., Dube, M., McLean, D. & Kates, M., 2003. Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresource technology,1,89, 1-16. https://doi.org/10.1016/S0960-8524(03)00040-

WHAT IS THE DIGESTATE?

As anaerobic digestion (AD) is quickly being harnessed in Italy and in other European countries, there is a need for a more in-depth description of the main by-product of the process, the digestate. Little information on digestate characteristics and composition is available and unclear legislation causes problems in biogas plant management. In this work, the organic matter (OM) of this matrix was described through chemical, biological, spectroscopic, and statistical approaches. It was shown that AD results in a strong reduction of the easily degradable fraction of the OM and an accumulation of recalcitrant molecules (possible humus precursors). This contributes to a relatively high biological stability of the residual OM content in the digestate and may lead to good amendment properties. Besides, the observed relative accumulation and the high mineralisation of nitrogen and phosphorus may point to the digestate as a readily available liquid fertiliser for agronomic use. Moreover, xenobiotics and pathogens respected limits for both biosolids and compost in Italian and European legislation