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Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum. • An AI course for chemical engineers is given as one of the first attempts in Europe. • The course is structured into frontal and hands-on lessons. • Modelling and optimisation techniques via AI were shown to the students. • Applications of developed models were explained to the students. • Students can apply basic AI in chemical engineering tasks after this course. [ABSTRACT FROM AUTHOR]

Levees are the most frequently used flood protection measure worldwide. In central Europe, they have been built systematically since the beginning of the 19th century. After the extreme regional floods in 1997, 2002 and 2013, the need arose for a more systematic and optimised approach to levee design and management, including the decommissioning of ineffective dike reaches. In this paper, a multicriterial algorithm is presented for the optimisation of levee management. The aspects considered are flood risk, economy, technical conditions, environmental issues and the consistency with general documents and principles in water management. The method is demonstrated via a case study of a levee subsystem in the upper Morava River basin. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential for deep renovation of an apartment building to the level of a passive house in different contexts in the continental part of Europe. The examined variables include different local climatic conditions, levels of economic development, and levels of market development (energy prices, energy footprint, labor prices, etc.) as well as different energy efficiency retrofit scenarios in four different countries. The adequate methodology was developed here in order to obtain an optimal solution for deep renovation in each context. The proposed methodology was based on the interaction of energy simulation and mathematical optimization. In this model, the energy performances of a building are determined with the EnergyPlus package and the optimal solution was obtained by using a mixed-integer non-linear programming model. The results demonstrate that the optimal solution for each analyzed location cannot provide cost-effectiveness over the lifetime of a building, so the introduction of additional subsidies or incentives, such as carbon taxes and upfront subsidies, is necessary. [ABSTRACT FROM AUTHOR]

Diversification potential enables investors to manage their risk and decrease risk exposure. Good diversification policy is a safety net that prevents a portfolio from losing its value. A well-diversified portfolio consists of different categories of property with low correlations, while highly correlated markets have the feature of low possibilities for diversification. The biggest riddle in the world of investments is to find the optimal portfolio within a set of available assets with limited capital. There are numerous studies and mathematical models that deal with portfolio investment strategies. These strategies take advantage of diversification by spreading risk over several financial assets. Modern portfolio theory seeks to find the optimal model with the best results. This paper tries to identify relationships between returns of companies traded in South-East European equity markets. A Markowitz mean-variance (MV) portfolio optimization method is used to identify possibilities for diversification among these markets and world leading capital markets. This research also offers insight into to the level of integration of South-East European equity markets. Principal component analysis (PCA) is used to determine components that describe the strong patterns and co-movements of the dataset. Finally, we combined MV efficient frontier and equity, which represent PCA components, to draw conclusions. Our findings show that PC analysis substantially simplifies asset selection process in portfolio management. The results of the paper have practical applications for portfolio investors. [ABSTRACT FROM AUTHOR]

A novel model for the clearing of European day-ahead electricity markets, with all associated types of orders, namely simple hourly orders, block orders, complex orders and PUN (“Prezzo Unico Nazionale”) orders is presented in this paper. An iterative algorithm is employed for the market clearing, bearing an inner iteration process for the handling of Paradoxically Accepted block and MIC orders, and an outer iteration process for the handling of PUN orders. During the iterative process a Master Problem and a PUN subproblem are sequentially solved and coordinated, for the efficient handling of all order clearing conditions and market and/or system constraints. The clearing conditions of each order type are explicitly incorporated in the PUN subproblem, formulating a Mixed Complementarity Problem bearing both primal and dual decision variables. The PUN subproblem concerns the sequential clearing of 24 hourly subproblems, employing a special technique for handling the possible inefficiencies due to the myopic nature of the hourly subproblem solutions, leading to enhanced behavior in terms of social surplus with respect to the state-of-the-art day-ahead market clearing solver. The model and the algorithmic process are evaluated in a west-European test case, demonstrating minimal computational requirements and proving its excellent escalation capabilities. [ABSTRACT FROM AUTHOR]

The first of this two-paper series addresses a practical day-ahead auction model, where generation revenue constraints are explicitly incorporated in the problem formulation, as routinely done in several national electricity markets across Europe. The revenue-constrained market-clearing procedure includes the effect of the transmission network, inter-temporal constraints associated with generation scheduling, demand-side bidding, and marginal pricing. This auction design is an instance of price-based market clearing which features two major complicating factors. First, locational marginal prices become decision variables of the optimization process. In addition, producer revenues are formulated as bilinear and highly nonconvex products of power outputs and market-clearing prices. The resulting problem is formulated as a mixed-integer nonlinear bilevel program with bilinear terms for which available solution techniques rely on heuristics, approximations, or modeling simplifications. This paper presents a novel and exact methodology whereby the original problem is recast as an equivalent single-level mixed-integer linear program. As a consequence, finite convergence to optimality is guaranteed and the use of standard commercial software is allowed. The proposed transformation is based on duality theory of linear programming, Karush-Kuhn-Tucker optimality conditions, and integer algebra results. In the second part of this two-paper series, numerical results from several case studies illustrate the effective performance of the proposed solution approach. [ABSTRACT FROM AUTHOR]

The aim of this paper is to showcase different decarbonization pathways for Europe with varying Carbon dioxide (CO2) constraints until 2050. The Global Energy System Model (GENeSYS-MOD) framework, a linear mathematical optimization model, is used to compute low-carbon scenarios for 17 European countries or regions. The sectors power, lowand high-temperature heating, and passenger and freight transportation are included, with the model endogenously constructing capacities in each period. Emission constraints differ between different scenarios and are either optimized endogenously by the model, or distributed on a per-capita basis, GDP-dependent, or based on current emissions. The results show a rapid phase-in of renewable energies, if a carbon budget in line with established international climate targets is chosen. It can be shown that the achievement of the 2° target can be met with low additional costs compared to the business as usual case, while reducing total emissions by more than 30%. [ABSTRACT FROM AUTHOR]

The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO in 1.7 and 1.0 μg/m, respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health. [ABSTRACT FROM AUTHOR]

This paper presents a brief review of how industrial mathematics, inspired by the Oxford Study Group activity, organized itself in Europe, gave rise to the European Consortium for Mathematics in Industry, the series of European Study Groups with Industry, and to new modes of productive contacts between industry and applied mathematicians in academia. [ABSTRACT FROM AUTHOR]

The economic crisis which started in 2007 has caused damaging effects to most international companies. In several EU countries, foreign investments decreased and international companies closed or relocated production and/or distribution centres, due to cost reduction measures. This paper is aiming to demonstrate that applying a performant solution in defining the supply chain strategy can be a low cost process, which can generate positive effects on the sales and revenues. The research objective is to present a concrete solution for redesigning the supply chain, in order to achieve the optimal delivery strategy. The solution implies the use of mathematic methods, for determining the optimum location for placing a central warehouse, in a geographic region. The use of an optimum delivery strategy leads to lower depositing and transport costs, which generates positive effects on sales, by offering more competitive prices on products. The main contribution of the author is the development of a model, used to identify the optimal location for placing a central warehouse. [ABSTRACT FROM AUTHOR]

The new reactor concepts proposed in the Generation IV International Forum require the development and validation of computational tools able to assess their safety performance. In the first part of this paper the models of the ESFR design developed by several organisations in the framework of the CP-ESFR project were presented and their reliability validated via a benchmarking exercise. This second part of the paper includes the application of those tools for the analysis of design basis accident (DBC) scenarios of the reference design. Further, this paper also introduces the main features of the core optimisation process carried out within the project with the objective to enhance the core safety performance through the reduction of the positive coolant density reactivity effect. The influence of this optimised core design on the reactor safety performance during the previously analysed transients is also discussed. The conclusion provides an overview of the work performed by the partners involved in the project towards the development and enhancement of computational tools specifically tailored to the evaluation of the safety performance of the Generation IV innovative nuclear reactor designs. [ABSTRACT FROM AUTHOR]

Network constraints play a key role in the price finding mechanism for European Power Markets, but historical data is very sparse and usually insufficient for many quantitative applications. We reconstruct the constraints data, known as the Power Transmission Distribution Factors (PTDFs) and Remaining Available Margins (RAMs), by first recovering the underlying time dependent signals known as the Generation Shift Keys (GSKs) and Phase Angles (PAs), and the electricity grid characteristics, via a mathematical optimisation problem. This is solved by exploiting marginal convexity in certain subspaces via alternating minimisation. The GSKs and PAs are then mapped to the PTDFs and RAMs, using the grid structure. Our reconstruction achieves in-sample relative errors of 22.3% and 15.9% for the PTDFs and RAMs respectively, while the out-of-sample ones are 25.4% and 18.1% respectively. We further show that our model outperforms the naive approach, and that the reconstructed GSKs and PAs recover specific structure. [ABSTRACT FROM AUTHOR]

Background Agri-food is one of the most important sectors of the industry in Europe and potentially a major contributor to the global warming. Sustainability issues in this context pose a huge challenge for several reasons: the variety of considered scales, the number of disciplines involved, the uncertainties, the out-of-equilibrium states, the complex quantitative and qualitative factors, the normative issues and the availability of data. Although important insight and breakthroughs have been attained in different scientific domains, an overarching and integrated analysis of these complex problems have yet to be realized. Scope and Approach This context creates huge opportunities for research in interaction with mathematical programming, integrative models and decision-support tools. The paper propose a computational viewpoint including questions of holistic approach, multiscale reconstruction and optimization. Some directions are discussed. Key Findings and Conclusions Several research questions based on a mathematical programming framework are emerging: how can such a framework manage uncertainty, cope with complex qualitative and quantitative information essential for social and environmental considerations, encompass diverse scales in space and time, cope with a multivariable dynamic environment and with scarcity of data. Moreover, how can it deal with different perspectives, types of models, research goals and data produced by conceptually disjoint scientific disciplines, ranging from physics and physiology to sociology and ethics? Building models is essential, but highly difficult; it will need a strong iterative interaction combining computational intensive methods, formal reasoning and the experts of the different fields. Some future research directions are proposed, involving all those dimensions: mathematical resilience, human-machine interactive learning and optimization techniques. [ABSTRACT FROM AUTHOR]

Numerous Life Cycle Analysis of bread pointed out that wheat production and transportation have important impacts on the environment. But wheat is grown all around the world and it is not always consumed where it is produced. In this paper an analysis of the wheat movements around Europe is done, taking special attention on the Spanish situation. An optimization of wheat exchanges is done eliminating the import–export overlapping. This optimization entails a reduction of the number of trips by 34% and the CO 2 emissions reduction due to transportation is calculated to be 121.175 tones. Consequently less trips, fewer trucks, less traffic and lower emissions that brings economic, social and environmental benefits. [ABSTRACT FROM AUTHOR]

Involving residential actors in the energy transition is crucial for its success. Local energy generation, consumption and trading are identified as desirable forms of involvement, especially in energy communities. The potentials for energy communities in the residential building stock are high but are largely untapped in multi-family buildings. In many countries, rapidly evolving legal frameworks aim at overcoming related barriers, e.g. ownership structures, principal–agent problems and system complexity. But academic literature is scarce regarding the techno-economic and environmental implications of such complex frameworks. This paper develops a mixed-integer linear program (MILP) optimisation model for assessing the implementation of multi-energy systems in an energy community in multi-family buildings with a special distinction between investor and user; the model is applied to the German Tenant Electricity Law. Based on hourly demands from appliances, heating and electric vehicles, the optimal energy system layout and dispatch are determined. The results contain a rich set of performance indicators that demonstrate how the legal framework affects the technologies' interdependencies and economic viability of energy communities with multi-energy systems. Certain economic technology combinations may fail to support national emissions mitigation goals and lead to lock-ins in Europe's largest residential building stock. The subsidies do not lead to the utilisation of a battery storage. Despite this, self-sufficiency ratios of more than 90% are observable for systems with combined heat and power plants and heat pumps. Social C O 2 mitigation costs range between 147.5–272.8 € / t CO 2 . Finally, the results show the strong influence of the heat demand on the system layout. • System optimisation of distinct operators and consumers in multi-family buildings. • Inclusion of Tenant Electricity Law leads to complex investment and dispatch decisions. • Highest profitability combining photovoltaic, combined heat and power and heat pump. • Combined heat and power is favoured, but profits depend on building's heating demand. • Legislation incentivises energy communities but may offset national CO 2 mitigation. [ABSTRACT FROM AUTHOR]

In Europe, most of the district heat is produced from fossil energy sources such as coal or natural gas. Together with the intention to divert municipal waste from landfill, which is still the main way of treatment in approximately half of the EU countries, there is a potential for the construction of waste-to-energy plants (WtEP) that can partially replace fossil fuels. An important factor in planning the construction of new WtE plants is the assessment of the project's economy. However, the ecological aspect of the project, namely the greenhouse-gas (GHG) savings, is becoming a topic of discussion nowadays. In terms of GHG savings, WtE plant integration into an existing district heating system (DHS) has a positive impact, but its level is often only roughly estimated. The paper presents a comprehensive mathematical optimization tool working on a daily time interval that is able to evaluate the impact of changing current technology or WtE plant construction on global warming potential. The technical parameters such as boiler output range, energy efficiency, etc. are considered. The tool also allows to put in the context the GHG savings and economic benefits of the project. These two factors are very difficult to compare. The study answers the question of how to deal with this issue and presents the possibility of comparison, which is generally transferable to any two parameters. Due to two poorly comparable criteria, the result does not include a single optimal solution, but clearly illustrates the view of the overall issue and gives a good basis for the final decision. The method used are illustrated in real-world DHS with various parameters where the relationship between GHG savings and the economic benefits of the integration of a WtE plant is evaluated. The whole problem is the task of linear integer programming and is implemented in the GAMS programming environment. • A multi-criteria optimization tool for WtE plant integration is introduced. • Economic and environmental aspects are put into context. • The optimal capacity is determined depending on the emphasis on both aspects. • The cost of removing a tonne of CO 2 by WtE in a particular location is calculated. [ABSTRACT FROM AUTHOR]

Prospective customers of financial and insurance products can be targeted based on the profit the provider expects to earn from them. We present a model for individual expected profit and two alternatives for calculating optimal personalized prices that maximize the expected profit. For one of these alternatives, we obtain a closed-form expression for the price offered to each prospective customer; for the other, we need to use a numerical approximation. In both approaches, the profits generated by prospective customers are not immediately observed, given that the products sold by these companies have a risk component. We assume that willingness to pay is heterogeneous and apply our methodology using real data from a European insurance company. Our study indicates that a substantial boost in profits can be expected when applying the simplest optimal pricing method proposed. [ABSTRACT FROM AUTHOR]

The ownership of a quoted company is usually spread among various shareholders. This dispersion can be characterized by an oriented network, whose nodes represent the companies, and an arc between companies i and j, with weight sij, indicates that company i owns sij percent of company j, being denoted by stock shareholding network (SSN). Given two sets of companies T and C0 from a SSN, we say that the companies in C0 control those in T if they own or imply ownership of at least αj percent of companies' T shares. So, given a targeting set T, our problem is to find a set of companies C0 in which we invest such that the entire investment cost is the lowest. This problem has relevant applications in corporate governance and it can be modeled within network optimization. We discuss its applicability using a broad set of companies in the European stock market. [ABSTRACT FROM AUTHOR]