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Renewable energy integration into existing or new energy hubs together with Green technologies such as Power to Gas and Green Hydrogen has become essential because of the aim of keeping the average global temperature rise within 2 °C with regard to the Paris Agreement. Hence, all energy markets are expected to face substantial transitions worldwide. On the other hand, investigation of renewable energy systems integrated with green chemical conversion, and in particular combination of green hydrogen and synthetic methanation, is still a scarce subject in the literature in terms of optimal and simultaneous design and operation for integrated energy grids under weather intermittency and demand uncertainty. In fact, the integration of such promising new technologies has been studied mainly in the operational phase, without considering design and management simultaneously. Thus, in this work, a multi-period mixed-integer linear programming (MILP) model is formulated to deal with the aforementioned challenges. Under current carbon dioxide limitations dictated by the Paris Agreement, this model computes the best configuration of the renewable and non-renewable-based generators, their optimal rated powers, capacities and scheduling sequences from a large candidate pool containing thirty-nine different equipment simultaneously. Moreover, the effect of the intermittent nature of renewable resources is analyzed comprehensively under three different scenarios for a specific location. Accordingly, a practical scenario generation method is proposed in this work. It is observed that photovoltaic, oil co-generator, reciprocating ICE, micro turbine, and bio-gasifier are the equipment that is commonly chosen under the three different scenarios. Results also show that concepts such as green hydrogen and power-to-gas are currently not preferable for the investigated location. On the other hand, analysis shows that if the emission limits are getting tightened, it is expected that constructing renewable resource-based grids will be economically more feasible., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2322-International Fellowship for Outstanding Researchers Program

Artificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to their non-convex nature. This study develops piecewise-linear formulations for the efficient approximation of the non-convex activation and objective functions in artificial neural networks for optimal, global and simultaneous training and feature selection in regression problems. Such formulations include binary variables to account for the existence of the features and piecewise-linear approximations, which in turn, after one exact linearization step, calls for solving a mixed-integer linear programming problem with a global optimum guarantee because of convexity. Suggested formulation is implemented on two industrial case studies. Results show that efficient approximations are obtained through the usage of the method with only a few number of breakpoints. Significant feature space reduction is observed bringing about notable improvement in test accuracy., 2232 International Fellowship for Outstanding Researchers Program; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Turkey is one of the richest countries in terms of renewable energy resources. At the same time, the largest portion of the account deficit of Turkey is due to energy import. Optimization studies for design, integration and management of renewable energy is therefore crucial in terms of increasing overall energy efficiency. In addition, energy sources and demands in Turkey have significant uncertainty and meeting the market conditions in a profitable manner is a challenging task. Stochastic programming is an efficient approach to deal with the aforementioned challenge. It requires introducing representative and comprehensive scenarios for the optimal design and scheduling. In this study, the aim is to propose a systematic and generic scenario generation method which is compatible with historical data, dependable for forecasts, and easily tunable for the scenario likelihood. Main contributions of this work are as follows: The uncertainty in the model parameters are propagated to the forecasts to obtain prediction intervals under a desired confidence, which provides probabilities of each scenario over the whole time horizon with predetermined likelihood the generated scenario set (e.g., likely, rare or statistically very low probability). Thus, scenario reduction is not needed. We implemented our method for Yalova, a developing city in Turkey, for the scenario generation of wind speed, population, air temperature, electricity consumption and solar irradiance, with a prediction horizon of 20 years. We also developed a preliminary mixed-integer linear programming (MILP) decision making model which computes both the optimal equipment investments and the optimal sub-hourly scheduling sequences of the equipment based on these scenarios and economic considerations.

Optimum selection of input variables, number of hidden neurons and connections between the network elements delivers the best configuration of an artificial neural network (ANN), resulting in reduced over-fitting and improved performance. In this study, a superstructure-oriented ANN design and training algorithm is suggested and implemented on an industrial Ethylene Oxide (EO) plant for the prediction of product related variables (i.e. EO production rate). Proposed formulation is a mixed integer nonlinear programming problem (MINLP), which takes the existence of inputs, neurons and connections of the network into account by binary variables in addition to continuous weights of existing connections. Investigations show that almost 90% of the connections are removed compared to the fully connected ANN (FC-ANN) with 50% decrease in the number of inputs of the ANN, approximately. The modified ANN delivers a better prediction performance over FC-ANN, which suffers from over-fitting.

Fluid Catalytic Cracking (FCC) is an important process which is used to convert heavy petroleum fractions into more valuable lighter products. In this work, the FCC process consists of the reactor, the regenerator and the fractionation units. Modeling is challenging due to the complex reaction chemistry and the interactions among the different process units. The reaction medium is modeled by the method of discrete lumping that uses narrow fractions. As a result, the number of discrete lumps (or pseudo-components) to model the process increases and this enables better prediction of fractionation products. For the reactor, we present a new kinetic model that includes a yield function for the cracking products. Kinetic constants and heat of cracking are correlated with the average boiling point of the pseudo-components. These correlations are next used in the development of first-principles models for the riser and the regenerator units. In addition, an empirical model is constructed for the purpose of predicting the individual amounts of the fractionation products from the reactor's effluent. Using parameter estimation, model parameters are estimated from actual industrial data. Model predictions match the plant measurements closely. Simulation and optimization results show that the developed model offers significant potential for use in real-time optimization and control.

Fluid catalytic cracking (FCC) is an important refinery process by which heavy hydrocarbons are cracked to form lighter valuable products over catalyst particles. FCC plants consist of the riser (reactor), the regenerator, and the fractionator that separates the riser effluent into the useful end products. In FCC plants the product specifications and feedstocks change due to varying economic and market conditions. In addition, FCC plants operate with large throughputs and a small improvement realized by optimization and control yields significant economic return. In previous work, we developed a nonlinear dynamic model and validated it with industrial data. In this study, our focus involves the development and application of a real-time optimization framework. We propose a hierarchical structure which includes a two-layer implementation of economic model predictive control (EMPC). EMPC provides the optimal riser and the regenerator temperature reference trajectories which are determined from a dynamic opt...

Hydrocracking is an important refinery process which is carried out in catalytic reactors to convert heavy petroleum fractions into valuable products. Because of the large number of species and complex reactions involved, modeling of hydrocracking is a challenging task. In this paper a dynamic, non-isothermal reactor model has been constructed using the method of continuous lumping which treats the complex reactive mixture as a continuum. In doing so concentrations are characterized in terms of reactivity which is a monotonic function of the true boiling point of the mixture. The material and energy balances are developed in the form of integro-differential equations. The significant modeling parameters are identified and estimated using data from an industrial reactor. Steady-state and dynamic predictions of the model outputs such as reactor temperature, product yields and hydrogen consumption are shown to be in good agreement with plant data.