No description available.

No description available.

Background: Closed-loop supply chains are a concept in sustainability that refers to reusingraw materials and reducing waste to fulfill the global demands for sustainable action.While profit-driven organizations’ interest in closed-loop supply chains has increased, there arefinancial barriers that limit the integration of the economic and the environmental dimension of sustainability. This study investigates profitability factors along with the closed-loop supplychain activities in their potential for overcoming the financial barriers within the context ofSwedish furniture manufacturers. Purpose: The purpose of this study is to examine closed-loop supply chains in their integrationof forward and reverse flows to achieve sustainability. The forward and reverse flows are investigated for factors of closed-loop supply chain profitability, within the context of the Swedish furniture industry. Method: This explorative study follows an inductive approach using a qualitative case studymethod. Data is triangulated from primary and secondary sources in the form of semi-structuredinterviews with purposefully chosen case companies, and secondary reports. Following a content analysis, data is coded and analyzed within and across cases. Findings: The study revealed seven financial barriers and a large range of profitability factorsthat help overcome financial barriers in closed-loop supply chains. The study generated a framework to show how profitability can be integrated along the closed-loop supply chain. The studyalso disclosed that product design and collaborative relationships offer the most potential forovercoming barriers among and between supply chains and for achieving a profitable CLSC.

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the Industrial Internet of Things (IIoT) allow for greater opportunities not only with digital twins but closed loop analytics. Discrete Event Simulation (DES) has been used to create digital twins and in some instances fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data with embedded machine learning to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with machine learning capabilities typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data

<p> Control structure design is an essential step in control system synthesis and has big impact on achievable closed-loop performance. This thesis develops a systematic approach of selecting optimal control structures based on closed-loop dynamic performance and other criteria, such as integrity.</p> <p> The main contribution of this thesis is a rigorous mathematical formulation for control structure design problem that includes full closed-loop transient analysis with additional integrity requirement. The multi-objective framework is extendable so that different control performance objectives can be easily added. Unique process requirements and engineer inputs can be taken into account as additional constraints. The proposed formulation is a Mixed Integer Nonlinear Programming (MINLP) with complementarity constraints. The research scope is limited to linear process models and linear controller algorithms.</p> <p> The tailored solving strategy that makes this challenging problem computationally tractable is introduced in this thesis. The modified Branch and Bound algorithm takes advantage of the special problem structure by using control knowledge to generate valid lower bound efficiently. Prior knowledge can be cooperated as heuristic tuning parameters to guide the solving process so that a reasonably good solution can be found early in the solving process. The complexity study shows the solving strategy can attack design problem size up to 8x8. Considering the percentage of good structures needing evaluation will decrease with problem size even larger problems will be tractable.</p> <p> The common control structures in process industries, such as square and nonsquare Single-Input-Single-Output (SISO) loop pairing using PID controller and block-centralized structure using Model Predictive Controller (MPC), are addressed in this thesis. The usefulness of this research has been demonstrated by several case studies, include Tennessee Eastman problem. The proposed methodology finds a physically sound pairing with good performance for Tennessee Eastman problem in less than one hour, while several off-the-shelf NLP, MINLP and global solvers cannot find a solution in five days.</p> / Thesis / Doctor of Philosophy (PhD)

No description available.

No description available.

No description available.

No description available.

No description available.