Optimization Modelling Of Industrial Systems (Gams, Pyomo)

Industry with Optimization

What you'll learn

Learn algorithms used in industry

Learn python, gams and pyomo

Participate in the Q&A forum, interact and ask questions

Learn how industrial systems are modelled in practice

Requirements

None

Description

Optimization has become a cornerstone in industrial applications, enabling industries to enhance efficiency, reduce costs, and improve sustainability. One prominent example is the optimization of furnaces used in manufacturing processes, such as steel production, glassmaking, and petrochemical refining. Furnace operations are highly energy-intensive, and optimization focuses on achieving maximum efficiency by regulating fuel consumption, heat transfer, and process parameters. Advanced mathematical modeling, coupled with real-time data, allows industries to simulate various operational scenarios and identify optimal settings that balance energy consumption with production quality and throughput.Beyond furnaces, optimization is widely applied in supply chain management, where it helps industries minimize costs and maximize service levels. For example, manufacturers use optimization algorithms to plan production schedules, reduce inventory holding costs, and streamline logistics. These models integrate constraints such as production capacity, demand fluctuations, and transportation limitations. The ability to simulate and optimize these complex, interconnected systems has proven invaluable in improving operational agility and meeting customer expectations, particularly in competitive markets.Industries also leverage optimization in designing new systems and technologies. For instance, in renewable energy, optimization techniques are used to design solar panels and wind turbines that maximize energy capture under varying environmental conditions. Similarly, the chemical and pharmaceutical industries utilize optimization to fine-tune reactor designs and drug manufacturing processes, ensuring safety and regulatory compliance while reducing waste. The integration of artificial intelligence and machine learning with optimization further enhances its applicability, enabling predictive capabilities and adaptive solutions that align with dynamic industrial demands. This synergy not only boosts productivity but also supports sustainability goals by reducing the environmental footprint of industrial activities.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: The furnace - chiller industrial system

Lecture 2 The Natural Gas Furnace-Chiller System 10m

Lecture 3 Forward Contracts for Natural Gas

Lecture 4 Heating and Cooling demand

Section 3: The Furnace Chiller Transformer System

Lecture 5 Introduction

Lecture 6 Electricity Demand

Lecture 7 Python model

Lecture 8 Python solution

Lecture 9 Modelling GAMS

Section 4: The Furnace Chiller Transformer Battery CHP System

Lecture 10 Introduction

Lecture 11 Python implementation

Lecture 12 Python solution

Lecture 13 GAMS Implementation

Section 5: The Furnace Chiller Transformer Battery CHP EHP System

Lecture 14 Introduction

Lecture 15 Python implementation

Lecture 16 Python solution

Lecture 17 GAMS implementation

Section 6: Conclusions

Lecture 18 Overview

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