Evaluating smart sampling for constructing multidimensional surrogate models

In this article, we extensively evaluate the smart sampling algorithm (SSA) developed by Garud et al. (2017a) for constructing multidimensional surrogate models. Our numerical evaluation shows that SSA outperforms Sobol sampling (QS) for polynomial and kriging surrogates on a diverse test bed of 13 functions. Furthermore, we compare the robustness of SSA against QS by evaluating them over ranges of domain dimensions and edge length/s. SSA shows consistently better performance than QS making it viable for a broad spectrum of applications. Besides this, we show that SSA performs very well compared to the existing adaptive techniques, especially for the high dimensional case. Finally, we demonstrate the practicality of SSA by employing it for three case studies. Overall, SSA is a promising approach for constructing multidimensional surrogates at significantly reduced computational cost.NRF (Natl Research Foundation, S’pore)Accepted versio

Enhanced procurement and production strategies for chemical plants : utilizing real-time financial data and advanced algorithms

This paper presents an implementation of an automated algorithm powered by market and physical data to improve procurement and production of a chemical plant with the goal of improving the overall economics on the entity. Herein, the algorithm is applied to two scenarios that serve as case studies: conversion of natural gas to methanol and crude palm oil to biodiesel. The program anticipates opportunities to increase profit or avoid loss by analyzing the futures market prices for both reagents and the products while considering cost of storage and conversion derived from physical simulations of the chemical process. Analysis conducted on June 11, 2018, in the biodiesel scenario shows that up to 219.28 USD per tonne of biodiesel can be earned by buying contracts for delivery of crude palm oil in July 2018 and selling contracts for delivery of biodiesel in August 2018 which equates to a margin 11.6% higher than in case of the direct trade. Moreover, it is shown that losses of up to 11.3% can be avoided, and therefore, it is shown that there is realistic scope for increasing the profitability of a chemical plant by exploiting the opportunities across different commodity markets in an automated manner. Consequently, such a cyber system can be used to assist eco-industrial parks with supply chain management, production planning, as well as financial risk governance and, in the end, help to establish a long-term strategy. This study is part of a holistic endeavor that applies cyber–physical systems to optimize eco-industrial parks so that energy use and emissions are minimized while economic output is maximized.National Research Foundation (NRF)Accepted versionThis project is funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program. The authors thank Churchill College, Cambridge, for their continual support

A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization

Based on share of energy, materials, resources and information, Eco Industrial Park (EIP) has become a popular form of industry cluster. Waste Heat Recovery (WHR) in EIP can significantly increase the total energy efficiency of the whole park, meanwhile reducing its greenhouse gas emission. The current paper proposes a methodology to assess the opportunities of WHR in EIP at park level. Four different steps are included in this methodology. The first step is identification of waste heat source plants and sink plants in EIP; the second step is the establishment of the waste heat transportation system; the third step is a Single-Objective Optimization Problem (SOOP); the fourth step is Multi-Objective Optimization Problem (MOOP). An EIP on Jurong Island Singapore comprising of five plants and two communities is used as a case study to demonstrate the capability of this methodology. Two different operation modes for the EIP are considered: with continuous waste heat and with discontinuous waste heat over time. The first scenario shows that SOOP and MOOP will deliver different WHR networks; the second scenario shows that waste heat discontinuity has great influence on the optimization of the WHR network. \ua9 201