Pinch analysis provides a systematic approach to targeting the minimum heating and cooling requirements of industrial processes and to designing heat exchanger networks that approach this target. Decades after its introduction, the method remains essential for energy efficiency, decarbonisation, and retrofit studies across sectors such as chemicals, pulp and paper, food, and refining. In addition, numerous papers have proposed extensions to PA but these are often left isolate and unavailable for future development and implementation. In short, tool development has not kept pace with academic progress and today’s industry requirements. Many practitioners rely on bespoke or basic spreadsheets or legacy desktop applications that make it difficult to scale analyses, embed the results in modern workflows, or experiment with emerging pinch techniques. OpenPinch fills this gap with an open-source Python toolkit that implements advanced pinch analysis and total site integration methods. The library offers typed schemas for robust data ingestion, modular analysis routines that span unit operation to process, site and regional scales, and export paths that maintain compatibility with conventional spreadsheet workflows. As illustrated by a survey of current pinch tools, a distinctive contribution is its comprehensive implementation of (unified) total site heat integration combined with multiple utilities, both isothermal and non-isothermal ones, via a combination of problem-table transformations, temperature-interval segmentation, and iterative assignment. The basis of these algorithms were reported in Tarighaleslami et al. (2017a). This technical note documents the architecture, algorithms, and data flow of OpenPinch, highlighting an end-to-end example of a pulp mill case study as well as key directions for future extension.

This study presents a novel multi-objective linear fractional interval programming model for sustainable waste management under uncertainty, with a focus on the Melbourne metropolitan region. The model integrates three conflicting objectives: minimizing the cost per recycled ton, minimizing emissions per processed ton, and minimizing the energy-related carbon footprint. In the existing literature, most traditional models rely on fixed decision variables, which often fail to capture the inherent uncertainty present in real-world, data-driven problems. Thus, in this study, all key parameters, including transport costs, recycling efficiencies, emissions, and facility capacities, are represented as interval values to account for parameter variability. The optimal solution allocates waste primarily to residential and green streams, yielding a cost per recycled ton in the range [21.01, 37.44] AUD, emissions per processed ton of [8.88, 20.75] kg CO $$_2$$ , and an energy-related carbon footprint of [4.44, 9.00] kg CO $$_2$$ . All solutions satisfy the prescribed budget and emission caps, demonstrating operational feasibility. The results further indicate that environmental objectives exhibit greater robustness to parameter uncertainty than economic costs, highlighting the effectiveness of the proposed interval-based framework for supporting sustainable municipal waste management decisions.