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The most used process for biological nitrogen removal from municipal and industrial wastewaters is the activated sludge process. Because of the importance of this process, as well as the large number of existing facilities, a lot of research effort has been focused on optimizing the operation strategies or improving the individual plant design. However, the systematic optimization of the process structure (process synthesis) and operation conditions based on rigorous process models has not been presented in the literature. The objective of this work is to address the simultaneous optimization of the process configuration and equipment dimensionsi.e., process synthesis and designand the operation conditions of activated sludge wastewater treatment plants for nitrogen removal based on a superstructure model. The model embeds up to five reactors and a secondary settler, and allows flow distribution of the main process streams, i.e., nitrate and sludge recycle streams and fresh feed, along the reaction zone. The objective function is to minimize the net present value formed by investment and operating costs, while verifying compliance with the effluent permitted limits. The investment cost computes the reaction tanks, aeration systems, secondary settler, influent pumping station, and sludge pump costs. The operation cost computes the cost for pumping, aeration, dosage of an external carbon source, excess sludge treatment for disposal, and fines according to pollution units discharged. Influent wastewater flowrate and composition are assumed to be known. The activated sludge model no. 3 and the Takács model are selected to describe the biochemical processes and the secondary settler, respectively. This results in a highly nonlinear system with nonsmooth functions. Because of the problem complexity, in this first approach, a nonlinear programming (NLP) problem (specifically a nonlinear programming with discontinuous derivatives (DNLP) problem) is proposed and solved to obtain some insights for future models. It was implemented and solved using general algebraic modeling system (GAMS). Results for case studies are presented and discussed.

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Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise.

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