Integrated Value Method for Sustainability Evaluation (MIVES) is a deterministic method based on requirement trees, value functions, and the Analytic Hierarchy Process. It allows integrating environmental, social, and economic sustainability indicators in a global index. The value functions make it possible to consider non-linearity in the assessment. MIVES takes into account the relative weight of the various model indicators. Deterministic models can cause significant problems in terms of adequately managing project sustainability. A method not only has to estimate the sustainability index at the end of the project. It also has to evaluate the degree of uncertainty that may make it difficult to achieve the sustainability objective. Uncertainty can affect indicators, weights, and value function shapes. This chapter presents a method for sustainability assessment, taking into account uncertainty. It is based on MIVES and the Monte Carlo simulation technique. An example of potential application is proposed, related to power plants.

This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller.

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Chile faced a severe aquatic animal health crisis in 2007 that affected the production of Atlantic salmon (Salmo salar) after an outbreak of infectious salmon anaemia (ISA). The outbreak had a considerable national economic impact. The response was led by the Competent Authority, the National Fisheries and Aquaculture Service (Sernapesca), which immediately implemented surveillance and control actions to mitigate the crisis. At the end of the initial response, the Competent Authority, together with the industry, set out a roadmap to return to sustainable salmon production. The success of the response was due to early detection and the implementation of biosecurity and control measures at all stages of production and control. These measures underpin the sanitary management model for aquaculture. The Chilean Veterinary Service has analysed critical health measures for salmon production and concluded that there has been an improvement in fish health, as evidenced by decreased mortalities, reduced use of antimicrobials, and improved management and control of prevalent diseases, such as salmon rickettsial syndrome (piscirickettsiosis), caligidosis and ISA. Improvements in health have contributed to increased harvests over time, with the largest monthly harvest for Atlantic salmon being achieved in January-February 2018, with 120,000 tonnes. The ISA crisis provided salutary lessons for the continued recovery and sustainability of Chile's salmon sector. The crisis highlighted the importance of strengthened Veterinary Services and public-private links, as well as a collaborative relationship with research entities and training centres. It was also important to enact new regulations to ensure recovery and sustainability. Fundamentally, the response to this crisis was based upon having good baseline surveillance already in place, supported by a Veterinary Service trained to manage emergency disease outbreaks.

This paper deals with the control problem of a wind turbine model working in the nominal zone. This process is a nonlinear system whose dynamics vary strongly depending on the operation point. In the nominal region, the wind turbine speed is controlled by means of the pitch angle to generate the nominal power. The wind fluctuations and its non-uniform special profile act as disturbances on the power generation and the tower deflections. These oscillations must be reduced to improve the wind turbine lifetime.In this work, an adaptive control structure operating on the pitch variable is proposed. It is composed of a gainscheduling PI control, an adaptive feedforward compensation of the wind speed and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as an optimization problem that minimizes the tower fore-aft displacements and the deviation of the wind turbine speed from its nominal value. It is resolved using genetic algorithms for different linear models that are obtained from the nonlinear model.The proposed controller is compared with a classical baseline PI (Proportional-integral) controller and the simulation results show a significant improvement of the system performance when the proposed strategy is applied.