Global optimisation of large-scale quadratic programs: application to short-term planning of industrial refinery-petrochemical complexes

This thesis is driven by an industrial problem arising in the short-term planning of an integrated refinery-petrochemical complex (IRPC) in Colombia. The IRPC of interest is composed of 60 industrial plants and a tank farm for crude mixing and fuel blending consisting of 30 additional units. It considers both domestic and imported crude oil supply, as well as refined product imports such as low sulphur diesel and alkylate. This gives rise to a large-scale mixed-integer quadratically constrained quadratic program (MIQCQP) comprising about 7,000 equality constraints with over 35,000 bilinear terms and 280 binary variables describing operating modes for the process units. Four realistic planning scenarios are recreated to study the performance of the algorithms developed through the thesis and compare them to commercial solvers. Local solvers such as SBB and DICOPT cannot reliably solve such large-scale MIQCQPs. Usually, it is challenging to even reach a feasible solution with these solvers, and a heuristic procedure is required to initialize the search. On the other hand, global solvers such as ANTIGONE and BARON determine a feasible solution for all the scenarios analysed, but they are unable to close the relaxation gap to less than 40% on average after 10h of CPU runtime. Overall, this industrial-size problem is thus intractable to global optimality in a monolithic way. The first main contribution of the thesis is a deterministic global optimisation algorithm based on cluster decomposition (CL) that divides the network into groups of process units according to their functionality. The algorithm runs through the sequences of clusters and proceeds by alternating between: (i) the (global) solution of a mixed-integer linear program (MILP), obtained by relaxing the bilinear terms based on their piecewise McCormick envelopes and a dynamic partition of their variable ranges, in order to determine an upper bound on the maximal profit; and (ii) the local solution of a quadratically-constrained quadratic program (QCQP), after fixing the binary variables and initializing the continuous variables to the relaxed MILP solution point, in order to determine a feasible solution (lower bound on the maximal profit). Applied to the base case scenario, the CL approach reaches a best solution of 2.964 MMUSD/day and a relaxation gap of 7.5%, a remarkable result for such challenging MIQCQP problem. The CL approach also vastly outperforms both ANTIGONE (2.634 MMUSD/day, 32% optimality gap) and BARON (2.687 MMUSD/day, 40% optimality gap). The second main contribution is a spatial Lagrangean decomposition, which entails decomposing the IRPC short-term planning problem into a collection of smaller subproblems that can be solved independently to determine an upper bound on the maximal profit. One advantage of this strategy is that each sub-problem can be solved to global optimality, potentially providing good initial points for the monolithic problem itself. It furthermore creates a virtual market for trading crude blends and intermediate refined–petrochemical streams and seeks an optimal trade-off in such a market, with the Lagrange multipliers acting as transfer prices. A decomposition over two to four is considered, which matches the crude management, refinery, petrochemical operations, and fuel blending sections of the IRPC. An optimality gap below 4% is achieved in all four scenarios considered, which is a significant improvement over the cluster decomposition algorithm.Open Acces

Estimating Emission Control Costs: A Comparison of the Approaches Implemented in the EC-EFOM-ENV and the IIASA-RAINS Models

The paper introduces two major model approaches to estimate emission control costs and develops a methodology to introduce results of energy flow optimization models (such as EFOM-ENV) into models for integrated assessment of acidification control strategies (such as the RAINS model). Based on a reference scenario for West Germany, national cost curves for reductions of SO2 and NOx emissions derived by both the EFOM-ENV and the RAINS model are compared. It is shown that -- as long as changes in the energy structure are excluded as means for reducing emissions -- results obtained from these models are comparable and the reasons for differences can be traced back to different input assumptions. However, as soon as energy conservation and fuel-substitution are utilized to reduce emissions, the simplified approach implemented in the RAINS model results in an overestimation of emission control costs

Environmental benefits of large‐scale second‐generation bioethanol production in the EU: an integrated supply chain network optimization and life cycle assessment approach

The use of agricultural residues for the generation of bioethanol has the potential to substitute fuels such as petrol or first‐generation bioethanol and thereby generate environmental benefits. Scientific research in this field typically confines the environmental dimension to global warming, disregarding other environmental impact and damage categories. By multi‐criteria mixed‐integer linear programming, this work examines environmental benefits and economic viability of optimal second‐generation bioethanol production network configurations to substitute petrol and/or first‐generation bioethanol in the EU. The results comprise environmentally optimal decisions for 18 impact and 3 damage categories, as well as economically optimal solutions for different excise and carbon tax scenarios. The impact categories global warming potential, particulate matter, and land use are affected the most. Optimal network decisions for different environmental objectives can be clustered into three groups of mutual congruencies, but opportunity costs between the different groups can be very high, indicating conflicting decisions. The decision to substitute petrol or first‐generation ethanol has the greatest influence. The results of the multi‐dimensional analysis suggest that the damage categories human health and ecosystem quality are suitable to unveil tradeoffs between conflicting environmental impacts, for example, global warming and land use. Taking human health and ecosystem quality as environmental decision criteria, second‐generation bioethanol should be used to concurrently substitute first‐generation bioethanol and petrol (100% and 18% of today's demand in the EU, respectively). However, economic optimization shows that with current taxation, bioethanol is hardly competitive with petrol, and that excise tax abatement or carbon taxes are needed to achieve these volumes. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.Horizon 2020 Framework Programme http://dx.doi.org/10.13039/10001066

Essays in International Economics: Decomposing Episodes of Large Growth in International Trade

My thesis consists of three chapters relating to topics in International Economics. In the first essay, I use bilateral trade data from Canada, Germany, Japan, Mexico, the U.S. and the U.K. to decompose the patterns of trade growth across various goods classifications during episodes of rapid growth in bilateral trade. I find that bilateral trade growth during these episodes is granular- less than 5\% of goods classifications account for over 65\% of overall bilateral trade growth. I quantitatively assess whether ``Melitz-style trade models, with heterogeneous productivity firms, CES demand and fixed and variable costs of exporting, can match the observed granularity of bilateral trade growth. I find the standard model generates only 10\% of the observed granularity in the data, as measured by the share of total trade growth accounted for by various quantiles of goods classifications. However, by incorporating heterogeneous productivity changes and tariff reductions imputed from the U.S. production and export data, I find that the model generates roughly 70\% as much granularity of trade growth across goods as in the data. When firms export their goods to foreign markets, they often choose between multiple distribution technologies in transporting their goods to their final destination. The second essay extends the standard trade model by incorporating a choice among two distribution technologies in the exporting process- one low-fixed, high-variable cost method, and one high-fixed, low-variable cost method- and assessing the implications for trade growth across goods. In this model, I find that heterogeneous productivity or tariff changes may lead firms to ``switch\u27\u27 their optimal distribution method- from not-traded to traded, or from the low-fixed cost to the high-fixed cost technology. This results in disproportionately larger trade growth for these types of firms, since they benefit from a double reduction in the variable costs of exporting- the direct effect of the fall in trade costs, and the indirect effect of switching to a lower variable cost distribution method. Calibrating this model to bilateral trade flows, I find that model simulations with multiple distribution technologies generate up to 90-95\% of the granularity in trade growth observed in the data. The third essay examines the role of variation in transportation options- what I denote the ``supply network\u27\u27- on observed price differences between locations for a specific good, retail gasoline. I use a unique data set of weekly gasoline prices across 44 Canadian cities to analyze how the existence of variation in the available modes of transportation for gasoline between cities (via pipeline, marine tanker, rail or truck) accounts for observed price differences across locations. I find that the supply network is significant- cities connected by lower per-unit cost methods like pipelines or seaports exhibit smaller mean- and weekly-price differences than those connected only by road or rail, after controlling for variables such as distance, regional effects and market size

The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model of the world economy, which is built on the GTAP dataset and additional data for the greenhouse gas and urban gas emissions. It is designed to develop projections of economic growth and anthropogenic emissions of greenhouse related gases and aerosols. The main purpose of this report is to provide documentation of a new version of EPPA, EPPA version 4. In comparison with EPPA3, it includes greater regional and sectoral detail, a wider range of advanced energy supply technologies, improved capability to represent a variety of different and more realistic climate policies, and enhanced treatment of physical stocks and flows of energy, emissions, and land use to facilitate linkage with the earth system components of the IGSM. Reconsideration of important parameters and assumptions led to some revisions in reference projections of GDP and greenhouse gas emissions. In EPPA4 the global economy grows by 12.5 times from 2000 to 2100 (2.5% per year) compared with an increase of 10.7 times (2.4% per year) in EPPA3. This is one of the important revisions that led to an increase in CO2 emissions to 25.7 GtC in 2100, up from 23 GtC in 2100 projected by EPPA3. There is considerable uncertainty in such projections because of uncertainty in various driving forces. To illustrate this uncertainty we consider scenarios where the global GDP grows 0.5% faster (slower) than the reference rate, and these scenarios result in CO2 emissions in 2100 of 34 (17) GtC. A sample greenhouse gas policy scenario that puts the world economy on a path toward stabilization of atmospheric CO2 at 550 ppmv is also simulated to illustrate the response of EPPA4 to a policy constraint.This research was supported by the U.S Department of Energy, U.S. Environmental Protection Agency, U.S. National Science Foundation, U.S. National Aeronautics and Space Administration, U.S. National Oceanographic and Atmospheric Administration; and the Industry and Foundation Sponsors of the MIT Joint Program on the Science and Policy of Global Change: Alstom Power (France), American Electric Power (USA), BP p.l.c. (UK/USA), Chevron Corporation (USA), CONCAWE (Belgium), DaimlerChrysler AG (Germany), Duke Energy (USA), J-Power (Japan), Electric Power Research Institute (USA), Electricité de France, ExxonMobil Corporation (USA), Ford Motor Company (USA), General Motors (USA), Murphy Oil Corporation (USA), Oglethorpe Power Corporation (USA), RWE Power (Germany), Shell Petroleum (Netherlands/UK), Southern Company (USA), Statoil ASA (Norway), Tennessee Valley Authority (USA), Tokyo Electric Power Company (Japan), Total (France), G. Unger Vetlesen Foundation (USA)

An empirical study of the variability in the composition of British freight trains

As part of the broader sustainability and economic efficiency agenda, European transport policy places considerable emphasis on improving rail’s competitiveness to increase its share of the freight market. Much attention is devoted to infrastructure characteristics which determine the number of freight trains which can operate and influence the operating characteristics of these trains. However, little attention has been devoted to the composition of the freight trains themselves, with scant published data relating to the practicalities of this important component of system utilisation and its impacts on rail freight viability and sustainability. This paper develops a better understanding of the extent to which freight train composition varies, through a large-scale empirical study of the composition of British freight trains. The investigation is based on a survey of almost 3,000 individual freight trains, with analysis at four levels of disaggregation, from the commodity groupings used in official statistics down to individual services. This provides considerable insight into rail freight operations with particular relevance to the efficiency of utilisation of trains using the available network paths. The results demonstrate the limitations of generalising about freight train formations since, within certain commodity groupings, considerable variability was identified even at fairly high levels of disaggregation

Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

Sulfur Emissions, Abatement Technologies and Related Costs for Europe in the RAINS Model Database

This paper describes the part of the Regional Pollution Information and Simulation (RAINS) model dealing with the potential and costs controlling emissions of sulfur dioxide. The paper describes the selected aggregation level of the emission generating activities and reviews the major options for controlling SO2 emissions. An algorithm for estimating emission control costs is presented. The cost calculation distinguishes 'general'(i.e., valid for all countries) and 'country-specific' parameters in order to capture characteristic technology- and site-specific factors influencing the actual costs of applying a certain measure under a given condition. The methodology is illustrated by two examples for typical control technologies (wet flue gas desulfurization and the use of low-sulfur gas oil). Finally, the method for constructing emission abatement cost curves showing the relationships between the level of remaining emissions and the associated costs is explained. The general parameters used in the cost calculations are presented in the main body of the report, while all country-specific parameters are contained in a number of appendices. In addition, these country-specific appendices present the energy scenarios as they are currently implemented in the RAINS model, and the resulting cost curves for SO2 control related to these energy scenarios

Export Taxes, World Prices, and Poverty in Argentina: a Dynamic CGE-Microsimulation Analysis

In this paper we implement a sequential dynamic computable general equilibrium model combined with microsimulations to assess (1) the short- and long-run economic impacts of a gradual reduction in the export tax that was introduced during the economic crisis that hit Argentina at the end of 2001, and (2) the impact of a decrease in the world prices of food products, one of the country’s main export product. Our results show that the elimination of the export tax would have different long run effects depending on the fiscal instrument that is used by the government to compensate for the loss in tax revenue. On the one hand, when the government budget is equilibrated by an increased deficit, the average annual growth rate for 2008-2015 is lower than in the baseline scenario. On the other hand, when the government budget is equilibrated by an increased direct tax rate, there is a long-run positive effect on growth. In any case, the employment level is lower and the price of food items is higher. Therefore, the poverty headcount ratio increases. As expected, a reduction in the world price of food items (i.e., a worsening in Argentina’s terms of trade) would impact negatively on the country’s GDP growth rate and poverty, particularly in the rural areas.Poverty, export taxes, food prices, Argentina, computable general equilibrium microsimulations