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Efficient utilization of waste heat from industrial processes can be a significant source of energy savings for production plants as well as the driver of sustainable operation and emissions abatement. Industrial waste heat usually is contained in liquid or gaseous outlet streams. Alt-hough the possible ways to utilize waste heat are discussed by a wide variety of papers, these either provide only a general overview of utilization options and opportunities or focus on a narrow range of industrial processes. The aim of the present paper is to discuss practical aspects of waste heat utilization in the European Union so that the reader gains perspective on (i) thermal classification of waste heat, (ii) liquid and gaseous waste streams and their temperatures typical for industrial use cases, (iii) technical, economic, physical, and environmental aspects barring full utilization of the contained heat, (iv) waste heat sources in various industries, and (v) standard-ized equipment and technologies applicable to industrial waste heat utilization, including their advantages, disadvantages and weak points.

A new strategy for fast, approximate analyses of fluid flow and heat transfer is presented. It is based on Finite Element Analysis (FEA) and is intended for large yet structurally fairly simple heat transfer equipment commonly used in process and power industries (e.g., cross-flow tube bundle heat exchangers), which can be described using sets of interconnected 1-D meshes. The underlying steadystate model couples an FEA-based (linear) predictor step with a nonlinear corrector step, which results in the ability to handle both laminar and turbulent flows. There are no limitations in terms of the allowed temperature range other than those potentially stemming from the usage of fluid physical property computer libraries. Since the fluid flow submodel has already been discussed in the referenced conference paper, the present article focuses on the prediction of the tube side and the shell side temperature fields. A simple cross-flow tube bundle heat exchanger from the literature and a heat recovery hot water boiler in an existing combined heat and power plant, for which stream data are available from its operator, are evaluated to assess the performance of the model. To gain further insight, the results obtained using the model for the heat recovery hot water boiler are also compared to the values yielded by an industrystandard heat transfer equipment design software package. Although the presented strategy is still a “work in progress” and requires thorough validation, the results obtained thus far suggest it may be a promising research direction.

The ability to model fluid flow and heat transfer in process equipment (e.g., shell-and-tube heat exchangers) is often critical. What is more, many different geometric variants may need to be evaluated during the design process. Although this can be done using detailed computational fluid dynamics (CFD) models, the time needed to evaluate a single variant can easily reach tens of hours on powerful computing hardware. Simplified CFD models providing solutions in much shorter time frames may, therefore, be employed instead. Still, even these models can prove to be too slow or not robust enough when used in optimization algorithms. Effort is thus devoted to further improving their performance by applying the symmetric successive overrelaxation (SSOR) preconditioning technique in which, in contrast to, e.g., incomplete lower–upper factorization (ILU), the respective preconditioning matrix can always be constructed. Because the efficacy of SSOR is influenced by the selection of forward and backward relaxation factors, whose direct calculation is prohibitively expensive, their combinations are experimentally investigated using several representative meshes. Performance is then compared in terms of the single-core computational time needed to reach a converged steady-state solution, and recommendations are made regarding relaxation factor combinations generally suitable for the discussed purpose. It is shown that SSOR can be used as a suitable fallback preconditioner for the fast-performing, but numerically sensitive, incomplete lower–upper factorization.