In this work, we present a new Big-M reformulation for Generalized Disjunctive Programs. The proposed MINLP reformulation is stronger than the traditional Big-M, and it does not require additional variables or constraints. We present the new Big-M, and analyze the strength in its continuous relaxation compared to that of the traditional Big-M. The new formulation is tested by solving several in...

Abstract Superstructure optimization is a powerful but computationally demanding task that can be used to select the optimal structure among many alternatives within single optimization. In chemical engineering, such problems naturally arise in process design, where different need considered simultaneously minimize specific objective function (e.g., production costs or global warming impact). C...

This paper presents a mixed-integer nonlinear programming (MINLP) model for the optimal design of mechanical counter-flow cooling towers, subjected to standard design constraints. The objective function consists of the minimization of the total annual cost, which includes the capital cost of the cooling tower (that depends on the filling material and the air flowrate) and the operating cost (th...

This work proposes a novel approach for the simultaneous synthesis of Heat Exchanger Networks (HEN) and Utility Systems. Given a set of hot and cold process streams and a set of available utility systems (e.g., gas turbine, steam cycle, boiler, etc), the method determines the optimal selection of utility systems, their arrangement and design (including steam generator), and the heat exchanger n...

Adaptive Random Searches (ARS) are simple and effective optimization methods used for handling complicated nonconvex / multimodal nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) problems. ARS iteratively adapt search characteristics according to the past successful / failure steps. Periodic search domain expansions and contractions improve significantly the reliabili...

Many applications must ingest and analyze data that are continuously generated over time from geographically distributed sources such as users, sensors and devices. This results in the need for efficient data analytics in geo-distributed systems. Energy efficiency is a fundamental requirement in these distributed systems, and its importance is reflected in much recent work on performance analys...

Platforms with multiple Field Programmable Gate Arrays (FPGAs), such as Amazon Web Services (AWS) F1 instances, can efficiently accelerate multi-kernel pipelined applications, e.g., Convolutional Neural Networks for machine vision tasks or transformer networks Natural Language Processing tasks. To reduce energy consumption when the FPGAs are underutilized, we propose a model to (1) find off-lin...

Here we present a center-cut algorithm for convex mixed-integer nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like many other algorithms for convex MINLP, the center-cut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order...

With predefined schedule, this paper presents a mathematical formulation for synthesis of water-using networks in batch plants with a central reservoir for targeting freshwater consumption. A superstructure that considers all water reuse/recycle possibilities is presented for modeling the time-dependent water-using network. The proposed superstructure-based representation for synthesis of batch...