Neural networks are widely used as surrogate models but they do not guarantee physically consistent predictions thereby preventing adoption in various applications. We propose a method that can enforce NNs to satisfy physical laws that are nonlinear in nature such as enthalpy balances. Our approach, inspired by Picard successive approximations method, aims to enforce multiplicatively separable constraints by sequentially freezing and projecting a set of the participating variables. We demonstrate our PicardKKThPINN for surrogate modeling of a catalytic packed bed reactor for methanol synthesis. Our results show that the method efficiently enforces nonlinear enthalpy and linear atomic balances at machine-level precision. Additionally, we show that enforcing conservation laws can improve accuracy in data-scarce conditions compared to vanilla multilayer perceptron.

The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.

Asserting phase stability entails the global solution of a nonconvex optimisation problem, typically the tangent plane distance minimisation (TPDM). To improve computational tractability, we propose classifier-based surrogate models to replace the TPDM. We seek models that represent several multicomponent mixtures simultaneously, across various component identities, temperatures, and compositions. We investigate both artificial neural networks (ANN) and support vector machines (SVM) and use Matthew's correlation coefficient (MCC) as performance metric for the corresponding binary classification problems. For SVM models, linear, polynomial, and radial basis function (RBF) kernels are assessed; while for ANNs, the tanh and relu activation functions are investigated. We test the performance of these surrogate models on a set of ternary mixtures that involve ibuprofen and two solvents with fixed or variable temperatures. The results show that ANNs and SVMs can both predict phase stability reliably, with RBF-SVM giving the lowest computational cost.

This paper revisits the approach of transforming a mixed-integer polynomial program (MIPOP) into a mixed-integer quadratically-constrained program (MIQCP), in the light of recent progress in global solvers for this latter class of models. We automate this transformation in a new reformulation engine called CANON, alongside preprocessing strategies including local search and bounds tightening. We conduct comparative tests on a collection of 137 MIPOPs gathered from test libraries such as MINLPLib. The solver GUROBI gives the best performance on the reformulated MIQCPs and outperforms the generic global solvers BARON and SCIP. The MIQCP reformulation also improves the performance of SCIP compared to direct MIPOP solution, whereas the performance of BARON is comparable on the original MIPOPs and reformulated MIQCPs. Overall, these results establish the effectiveness of quadratic reformulation for MIPOP global optimization and support its integration into global solvers.

Mixed-integer polynomial programs (MIPOPs) frequently arise in chemical engineering applications such as pooling, blending and operations planning. Many global optimization solvers rely on mixed-integer linear (MIP) relaxations of MIPOPs and solve them repeatedly as part of a branch-and-bound algorithm using commercial MIP solvers. GUROBI, one of the prominent MIP solvers, recently added the capability to solve mixed-integer quadratically-constrained quadratic programs (MIQCQPs). This paper investigates global optimization of MIPOPs via their reformulation as MIQCQPs followed by their solution to global optimality using GUROBI. The effectiveness of this approach is tested on 60 instances of MIPOPs selected from the library MINLPLib. The performance of the MIQCQP reformulation approach is compared to the state-of-the-art global solvers BARON, ANTIGONE and SCIP in GAMS. For the case of single threading, a reduction of 28% and 42% compared to SCIP and ANTIGONE respectively is observed. This approach, therefore, holds promise for integration into existing global solvers to handle MIPOPs.

As the costs of solar PV continuously decrease and pollution legislation imposes less burning of agricultural residues, decentralized renewable energy is increasingly affordable for providing electricity to one billion people lacking access to a power grid. This paper presents a techno-economic feasibility case study of biomass gasification in off-grid and grid-connected mini-grids for community-scale energy application in rural Uttar Pradesh, India. Energy demand data was collected through surveys in a village with irrigation and agro-processing loads and off-grid households and used to construct a seasonal load profile based on statistical methods. This was used to simulate single-source and hybrid mini-grids based on solar PV, biomass gasification and diesel generation using HOMER Pro. Hybrid PV-biomass or PV-diesel systems were found to offer the highest reliability for off-grid power at the lowest cost. Single-source PV was cheaper than biomass gasification, though the cost of electricity is highly sensitive to biomass supply and gasifier maintenance. Both renewable options were around half the cost of diesel generation. The findings held across grid-connected systems with weak, moderate and strong reliability of grid supply. This suggests that biomass gasification-based mini-grids are not cost-competitive with PV unless the two generation sources are combined in a hybrid system, though they require operational testing prior to implementation.

Greenhouse gas emissions contribute immensely to global warming. One of the major constituents of greenhouse gas emissions is carbon dioxide (CO2). It is highly essential to reduce its emissions into the atmosphere. One of the ways to reduce its emissions is to capture and store it.

Currently, monoethanolamine (MEA) is used to separate out CO2 from the flue gas. This process is expensive and it comes with a significant energy penalty for the power plants. As an alternative, calcium looping process was proposed. This process makes use of the reversible reaction between calcium oxide (CaO) and CO2 to give calcium carbonate (CaCO3). When separation is desired flue gas is sent through the sorbent (CaO), then it is sent through the regenerator which provides a pure stream of CO2 for carbon capture. This process allows attainment of high carbon capture rates. Furthermore, it comes with a lower energy penalty for power plants. In addition, it can be easily retrofitted to power plants. At present, no known industrial implementations of calcium looping exist. The research thus far is restrained to pilot plant stage.

Calcium looping has been proposed recently and a lot of rapid developments are taking place. Bigger pilot-plants are designed for testing newer models developed from the recent experimental findings. Also, newer sorbents are being tested which give an improved performance.

In this paper, we will review the current status of research on calcium looping. Our objective is to give a general overview of recent developments in this field. We have addressed its feasibility on industrial scale. We discuss the different models used to describe this process, optimization of different parameters which affect the process, problems associated with it and modifications to this process to make it more efficient and improve it. We also review the economics of calcium looping process in comparison with the conventional process. Furthermore, we have also discussed the potential of wide variety of applications in which calcium looping cycles can be used.