Iskandar Halim

Sequential methodology for integrated optimization of energy and water use during batch process scheduling

Abstract Though commonly encountered in practice, energy and water minimization simultaneously during batch process scheduling has been largely neglected in literature. In this paper, we present a novel framework for incorporating simultaneous energy and water minimization in batch process scheduling. The overall problem is decomposed into three parts – scheduling, heat integration, and water reuse optimization – and solved sequentially. Our approach is based on the precept that in any production plant, utilities (energy and water) consumption is subordinate to the production target. Hence, batch scheduling is solved first to meet an economic objective function. Next, alternate schedules are generated through a stochastic search-based integer cut procedure. For each resulting schedule, minimum energy and water reuse targets are established and networks identified. As illustrated using two well-known case studies, a key feature of this approach is its ability to handle problems that are too complex to be solved using simultaneous methods.

Decision support for green supply chain operations by integrating dynamic simulation and LCA indicators: diaper case study.

As the issue of environmental sustainability is becoming an important business factor, companies are now looking for decision support tools to assess the fuller picture of the environmental impacts associated with their manufacturing operations and supply chain (SC) activities. Lifecycle assessment (LCA) is widely used to measure the environmental consequences assignable to a product. However, it is usually limited to a high-level snapshot of the environmental implications over the product value chain without consideration of the dynamics arising from the multitiered structure and the interactions along the SC. This paper proposes a framework for green supply chain management by integrating a SC dynamic simulation and LCA indicators to evaluate both the economic and environmental impacts of various SC decisions such as inventories, distribution network configuration, and ordering policy. The advantages of this framework are demonstrated through an industrially motivated case study involving diaper production. Three distinct scenarios are evaluated to highlight how the proposed approach enables integrated decision support for green SC design and operation.

A knowledge-based simulation-optimization framework and system for sustainable process operations

Abstract Design and operation of chemical plants involves a combination of synthesis, analysis and evaluation of alternatives. Such activities have traditionally been driven by economic factors first, followed by engineering, safety and environmental considerations. Recently, chemical companies have embraced the concept of sustainable development, entailing renewable feed materials and energy, non-toxic and biodegradable products, and waste minimization or even elimination at source. In this paper, we introduce a knowledge-based simulation-optimization framework for generating sustainable alternatives to chemical processes. The framework has been developed by combining different process systems engineering methodologies – the knowledge-based approach for identifying the root cause of waste generation, the hierarchical design method for generating alternative designs, sustainability metrics, and multi-objective optimization – into one coherent simulation-optimization framework. This is implemented as a decision-support system using Gensyms G2 and the HYSYS process simulator. We illustrate the framework and system using the HDA and biodiesel production case studies.

A material-centric methodology for developing inherently safer environmentally benign processes

Abstract Stringent safety and environmental regulations, and competition have challenged the chemical process industries to bring products to market at low lifecycle costs without compromising on safety and environmental standards. This has led the designers to consider inherent safety and waste minimization principles at the early stages of design. Tools and methods are available for developing inherently safer process and carrying out waste minimization analysis individually without taking into account the close coupling between them. This results in an incomplete and inaccurate analysis. In this paper, we present a systematic methodology for the integrated safety and waste minimization analysis during process design. Safety issues are characterized by unintended effects of materials and waste issues by release of material to the environment. The material-centric view thus brings out the similarities between the source of safety and pollution issues and the strategies necessary for their improvement. It also provides a common framework to identify synergies and tradeoffs among the alternatives generated thus enhancing the decision-making during design process. The integrated methodology is implemented as an intelligent decision support system that can help design inherently safe and environmentally benign chemical processes. The integrated methodology and its implementation are discussed and illustrated on an industrial process involving acrylic acid production process.

Green Supply Chain Design and Operation by Integrating LCA and Dynamic Simulation

Abstract With sustainability increasingly becoming an important business factor, companies are now looking for methods and tools to help assess the fuller picture of the environmental impacts associated with their manufacturing and supply chain activities. Life cycle assessment (LCA) is a widely-used technique for measuring the environmental costs assignable to a product or service. However, LCA takes a high-level view and assumes a fixed supply chain structure and operation. It does not explicitly consider the effect of supply chain design and practices which can be a significant contributor to the overall environmental impacts. This paper presents an approach integrating LCA indicators and dynamic simulation for green supply chain design and operation. Environmental impact indicators are incorporated into a dynamic model of the supply chain along with profit and customer satisfaction, so that sustainability of various design and operational decisions can be assessed comprehensively. The application and benefits of the proposed approach are demonstrated using two case studies.

Integrating Economic, Environmental and Social Indicators for Sustainable Supply Chains

Abstract The past years have seen a growing attention to incorporating sustainability into the manufacturing supply chain. However, compared to the economic and environmental dimensions, the social dimension of sustainability has not been as well-defined. This is due to the difficulties in quantifying social themes into metric indicators. This paper proposes a framework for evaluating the sustainability of a detergent supply chain through integration of supply chain dynamic simulation and environmental- and societal- Life Cycle Assessment (LCA).

Multi-objective scheduling for environmentally-friendly batch operations

Abstract The push towards sustainable operation has pressurized the batch process industries to implement energy minimization. One technique proven in the continuous industries is heat integration—matching the hot streams that require cooling and the cold streams that require heating to reduce the overall utilities consumption. In this work, we present a methodology for heat integration in multipurpose batch plants. This is done by optimizing the schedule to simultaneously minimize an economic objective such as make-span and utilities. We illustrate the framework by solving a literature case study.

Systematic Framework for Design of Environmentally Sustainable Pharmaceutical Supply Chain Network

PurposeThe current push towards sustainability has pressurized pharmaceutical companies to reduce greenhouse gas (GHG) emissions in their manufacturing supply chains (SCs). However, the heavily regulated nature of the pharmaceutical industry has necessitated decisions such as sourcing of raw materials including names and addresses of suppliers and siting of plants to be locked early during the registration of a new drug. This could result in SC inefficiencies during the drug commercial life leading to higher than necessary GHG emissions. This paper presents a systematic framework for design of a more sustainable pharmaceutical SC network at the commercial stage that can be performed during the early stages of drug development.MethodsThe framework comprises the following steps. First, basic SC information including process chemistries, outsourcing strategies, and potential supplier and manufacturer sites is consolidated. Next, an analytic hierarchy process (AHP) is performed to identify the most suitable supplier and manufacturer sites followed by mapping the entire SC network by connecting all the sites that have been identified as high priority. Subsequently, a set of indicator metrics—namely, cost, lead time, and GHG emissions—is calculated to evaluate the economic and environmental performances of the network.ResultsThe framework has been applied to an industrially motivated case study. Two network alternatives were proposed and analyzed based on their metrics together with synergies and trade-offs highlighted.ConclusionsThe findings demonstrate the efficacy of the framework in generating different network alternatives and identifying the most sustainable one on the basis of economic and environmental benefits. As such, the framework is applicable to the early stages of drug development where information is very limited.

An intelligent system for green process design

Growing environmental issues have motivated technological advances to reduce pollution from chemical processing plants. These include the use of process simulators to design an environmentally benign process that minimises the plant emissions. While these simulators offer a convenient way of simulating the plants performance, currently they have limited innate capability to support environmental studies. In this article, we present a novel framework that integrates expert system with process simulator and mathematical optimisation as a means for waste minimisation analysis and decision support. We illustrate the framework by solving a literature case study involving hydrocarbon separation process.

Q-SA√ESS: a methodology to help solvent selection for pharmaceutical manufacture at the early process development stage

A practicable, LCA based methodology has been developed to evaluate the sustainability implications of solvent selection during early process development for a batch manufacturing process for an API. Q-SA√ESS (Quick Sustainability Assessment via Experimental Solvent Selection) structures the early use of existing tools to carry out initial solvent selection on the basis of EHS factors and suitability for the desired chemistry. The life cycle of the solvent has been divided into three stages: solvent manufacture, use in the batch process and end of life/recycling. The application of the methodology is illustrated for a single example of chemistry which has been taken through from development lab to process evaluation towards pilot scale. This demonstrates how the methodology helps to select the solvent giving the lowest overall environmental impact.