Athanassios Nikolakopoulos

A line up evolutionary algorithm for solving nonlinear constrained optimization problems

In this work a complete framework is presented for solving nonlinear constrained optimization problems, based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems. Linear and/or nonlinear constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The LUDE algorithm maintains a population of solutions, which is continuously improved as it thrives from generation to generation. In each generation the solutions are lined up according to the corresponding objective function values. The positions in the line are very important, since they determine to what extent the crossover and the mutation operators are applied to each particular solution. The efficiency of the proposed methodology is illustrated by solving numerous unconstrained and constrained optimization problems and comparing it with other optimization techniques that can be found in the literature.

A threshold accepting heuristic with intense local search for the solution of special instances of the traveling salesman problem

In real life scheduling, variations of the standard traveling salesman problem are very often encountered. The aim of this work is to present a new heuristic method for solving three such special instances with a common approach. The proposed algorithm uses a variant of the threshold accepting method, enhanced with intense local search, while the candidate solutions are produced through an insertion heuristic scheme. The main characteristic of the algorithm is that it does not require modifications and parameter tuning in order to cope with the three different problems. Computational results on a variety of real life and artificial problems are presented at the end of this work and prove the efficiency and the ascendancy of the proposed method over other algorithms found in the literature.

BIOCORE- A systems integration paradigm in the real-life development of a lignocellulosic biorefinery

Abstract In the European Community the production of biofuels has seen a steep increase ( IEA, 2010 ) but the production will not be competitive unless combined with a parallel production of chemicals. The emphasis on lignocellulosic supplies is entirely consistent with the underlying principles of sustainable development but industrial designs require a significant degree of systems integration across all the stages of process development. The overbearing role of a systems approach has been pronounced in the literature ( Dimian, 2007 , Sammons, 2008 ). The paper outlines a functional paradigm of real-life integration in an emerging European biorefinery with origins in the Pulp and Paper Industry. Mounted by new technology (CIMV process) and a systems paradigm to screen industrial chemistries (available from the BIOCORE experimental groups), the initial process advances into a real-life lignocellulosic biorefinery. The paper explains the systems paradigm further highlighting areas and methods that appear to define open problems in research.

A water saving methodology for the efficient development of biorefineries

Abstract The paper addresses the complexities of biorefinery models in regards of minimizing water consumption, and offers paradigms of integration between mathematical programming methods and Water Pinch Analysis. In a new framework the Relative Residual Analysis (RRA) is introduced as a targeting tool of enhanced precision. Integrated water network designs are driven by RRA and accomplished through optimization of superstructure models (SM). The approach is illustrated through a water minimization problem in a real life bio-refinery, and mathematical formulations take the form of MILP and NLP models. Water targets report ∼18 % savings of use in the case of re-use, and ∼58% in the case of recycle-regeneration.

A metaheuristic approach for the sequencing by hybridization problem with positive and negative errors

This work introduces a metaheuristic method for the reconstruction of the DNA string from its l-mer content in the presence of large amounts of positive and negative errors. The procedure consists of three parts: the formulation of the problem as an asymmetric traveling salesman problem (ATSP), a technique for handling the positive errors and an optimization algorithm that solves the formulated problem. The optimization algorithm is a variation of the threshold accepting method with intense local search and its function is controlled by a size diminishing shell. The optimization algorithm is used consecutively on ATSPs of continuously decreasing sizes till it reaches a final solution. The proposed method provides solutions of better quality compared to algorithms in the recent bibliography.

A Mathematical Decomposition for the Synthesis and the Application of Total Site Analysis on Multi-product Biorefineries

Abstract Multi-product biorefineries emerge with the need for novel conceptual process synthesis tools that will simultaneously assess a multitude of new processes and products for being integrated into the complete biorefinery scheme. Energy integration plays a key role in producing sustainable and profitable processes. Direct or indirect process to process integration may allow the inclusion of-otherwise rejected-energy intensive processes into a final biorefinery system. This paper presents an automated two staged Total Site approach combined with economic indicators, for selecting optimal combinations of candidate processes and producing optimal allocation of utilities. The proposed method can further evaluate process modifications and aid design decisions. The applicability of the models is illustrated through a case study involving eight real pilot biorefining processes developed in the course of the European biorefinery research project Biocore (2010).

Mathematical Programming Shortcut Screening Models for the Design of Integrated Waste Treatment Systems

Abstract The paper proposes an extension of the transhipment models of Nikolakopoulos et al. (2012) in targeting the minimum waste treatment flowrate for single and multiple contaminant systems. Problems are modelled as inverse residual cascades with two concentration shifting operations for transforming the models into virtual resource targeting formulations and cope effectively with the concentrations of the multiple contaminants. The methodology has been successfully tested in problems of systems with various contaminant and treatment process features.

Targeting and synthesis of single-impurity total water systems using coordinated transhipment models

Reuse, regeneration and recycle are common practices for attaining maximum savings in industrial water networks. Additional benefits are generated when the selection of the regeneration technology takes place prior to the detailed design of the network and at the same time with the calculation of targets for fresh water use and wastewater treatment flows. This paper proposes a new methodology for targeting fresh water requirements and wastewater treatment flowrates, synchronized with the selection of the most cost-effective treatment technologies. The problems under study involve water systems with single impurities and water-using operations with fixed contaminant loads and/or fixed flowrates. The treatment processes are characterized by either fixed outlet concentrations or fixed removal ratios. Total water systems are simulated by two transhipment models; one for the system of water-using operations and a second for the wastewater treatment system. The procedure coordinates the two models into simultaneously targeting clear water use, calculating minimum recycle and treatment flows and selecting wastewater treatment technologies. The procedure offers the advantage of producing optimal solutions ahead of design, while critical system parameters can be detected through the pinch technology built in the transhipment models, offering the possibility to identify possibilities for improvement through process modifications. The new approach is illustrated using five examples with different system characteristics, and the results are validated and represented graphically using a superstructure optimization model of the systems.

A Mathematical Programming Targeting Method to Select Treatment Technologies Ahead of Design

Abstract The paper proposes a two stage mathematical programming approach for screening wastewater treatment technologies ahead of design, when the limiting concentrations and mass loads of contaminants for a set of water using operations are the only available data. The first stage of the methodology calculates the target for minimum fresh water flowrate further identifying the concentrations and the flowrates of the flows of the wastewater mains. The second stage selects the most effective treatment technologies and calculates targets for minimum wastewater treatment flows. The method is illustrated by a set of examples featuring the different aspects of the problem and characteristics of the solution.

Integrated Transhipment Models for Synchronous Screening of Treatment Technologies and Targeting of Fresh Water and Recycle Flows

Abstract In water integration the selection of regeneration technologies cannot be addressed separately from targeting recycle and fresh water flows. We propose a new methodology, which simulates total water systems using two transhipment models. The models are coordinated to target minimum fresh water requirements, calculate the optimal recycle and treatment flows and select water treatment processes. The procedure offers the advantage of producing optimal solutions ahead of design and is illustrated by means of two examples from literature.